representation of computer graphics

Introduction to Computer Graphics

(3 reviews)

David J. Eck, Hobart and William Smith Colleges

Copyright Year: 2016

Publisher: David J. Eck

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Marietta Cameron, Associate Professor, University of North Carolina Asheville on 2/1/18

The author intentionally and most understandably designed this text to present material for a one semester undergraduate course. Thus the topic coverage is a subset of material typically presented in texts addressing computer graphics. I... read more

Comprehensiveness rating: 3 see less

The author intentionally and most understandably designed this text to present material for a one semester undergraduate course. Thus the topic coverage is a subset of material typically presented in texts addressing computer graphics. I appreciate the implementations in Java, C, and JavaScript. Since the OpenSource 3D animation software Blender with its python interface is presented, maybe later editions of this text will include implementations in python. The hyperlinked terms and the demos embedded in the text’s online version enhance student learning. I respectfully disagree with the author’s decision to present foundational concepts with the much older OpenGL 1.1 and glut when he could do the same with OpenGL 4.5( or later) and freeglut. I also think that students would find chapter exercises helpful.

Content Accuracy rating: 4

This book's content is accurate with a few typos.

Relevance/Longevity rating: 2

As I have already mentioned, I strongly suggest the author drop all references to OpenGL 1.1 except in a section on the history of OpenGL. I agree that geometric modeling, transformations, color, lighting, textures, animation are fundamental but students (and industry) seem to ignore academics who present any ideas with what they (students and industry experts) perceive as outdated technologies.

Clarity rating: 5

The text is as clear as its "commercially available" counterparts.

Consistency rating: 4

The text is mostly consistent in terms of terminology and framework. A minor complaint is the occasional tense switch between first person singular and first person plural.

Modularity rating: 4

The text is written with an approach in mind....the topics can be rearranged with moderate effort.

Organization/Structure/Flow rating: 5

The organization is understandable, logical, and clear.

Interface rating: 3

It would be helpful if all figures and demos were numbered and labeled. Maybe later editions could include interactive exercises that would assess student understanding.

Grammatical Errors rating: 5

A few typos but nothing glaring.

Cultural Relevance rating: 3

This text avoids cultural references. Maybe it could offer more references to graphical software, games, animations, and special interest groups such as ACM SIGGRAPH, SIGCHI, and IGDA (International Game Developers Association).

I would include this text as a supplemental resource to for my students.

Reviewed by Jong Kwan Lee, Associate Professor, Bowling Green State University on 2/1/18

The contents of the book cover many topics in computer graphics that should be enough for an introductory level computer graphics course. However, some contents might need a little more explanation (e.g., Bezier curves) and I would like it to... read more

Comprehensiveness rating: 4 see less

The contents of the book cover many topics in computer graphics that should be enough for an introductory level computer graphics course. However, some contents might need a little more explanation (e.g., Bezier curves) and I would like it to include a coverage in clipping algorithms.

Content Accuracy rating: 5

The contents of the book seem very accurate. There are a variety of computer graphics topics covered, some more in details than others, with simple examples and interactive demos and these seem to be accurate.

Relevance/Longevity rating: 4

The book primary covers the basics in computer graphics. While some contents are not the most up-to-date materials, they are very adequate for introductory level computer graphics courses. I would personally use a different platform than Java in a computer graphics course, but the contents can be adapted to others.

Clarity rating: 4

Most of the contents are clear and adequate for a computer graphics textbook. However, there are a couple of topics that I would have like it to cover more in depth so that it might be easier for students to understand the topics.

Consistency rating: 5

The author presented the contents very consistently. The same approach is used to explain various computer graphics topics.

Modularity rating: 5

The took is easily and readily divisible into smaller reading sections. For example, I would cover some subsections from different chapters with other subsections and the book contents seem to be modular to do so.

Organization/Structure/Flow rating: 4

In general, I like the organization so that the contents are introduced in a logical way. However, there were a couple of sub-topics that could be combined with other sub-topics if I were to teach a computer graphics course with the book.

Interface rating: 5

There was no interface issue I could find. Links and demos that I checked all work properly.

No grammar mistake was found.

Cultural Relevance rating: 5

I don't think this question is related to the contents of the book. But the book had no issue for the point.

The book covers both 2D and 3D computer graphics topics with examples and demos. It should be a good textbook for an introductory level computer graphics book.

Reviewed by Brian Barsky, Professor of Computer Science, University of California, Berkeley on 2/1/18

This book is a guide for how write graphics programs using OpenGL and WebGL. It does not provide a an explanation of the concepts, methods, mathematics, physics algorithms, science, psychology, etc., etc., that one would find in a standard... read more

Comprehensiveness rating: 1 see less

This book is a guide for how write graphics programs using OpenGL and WebGL. It does not provide a an explanation of the concepts, methods, mathematics, physics algorithms, science, psychology, etc., etc., that one would find in a standard graphics text book, such as the ones by Foley et al., Shirley, Hearn & Baker, etc., etc.

Relevance/Longevity rating: 5

That is fine

That is fine.

Seemed fine.

no problems (and not a relevant question)

I would like to reiterate my overarching concern that this book is a guide for how write graphics programs using OpenGL and WebGL and does not provide a an explanation of the concepts, methods, mathematics, physics algorithms, science, psychology, etc., etc., that one would find in a standard graphics text book, such as the ones by Foley et al., Shirley, Hearn & Baker, etc., etc. As such, it is reminiscent of the Open GL Programming Guide by Mason Woo, Jackie Neider, Tom Davis, and Dave Shreiner. This would be a useful adjunct to a real computer graphics text book, such as the ones by Foley et al., Hearn & Baker, or Shirley but would not provide a suitable replacement for a real computer graphics textbook like that.

Table of Contents

• Chapter 1: Introduction
• Chapter 2: Two-Dimensional Graphics
• Chapter 3: OpenGL 1.1: Geometry
• Chapter 4: OpenGL 1.1: Light and Material
• Chapter 5: Three.js: A 3D Scene Graph API
• Chapter 6: Introduction to WebGL
• Chapter 7: 3D Graphics with WebGL
• Chapter 8: Beyond Realtime Graphics
• Appendix A: Programming Languages
• Appendix B: Blender: A 3D Modeling Program
• Appendix C: Gimp and Inkscape for 2D Graphics
• Appendix D: Source Code for Sample Programs
• Appendix E: Glossary

Ancillary Material

• David J. Eck

About the Book

Introduction to Computer Graphics is a free, on-line textbook covering the fundamentals of computer graphics and computer graphics programming. This book is meant for use as a textbook in a one-semester course that would typically be taken by undergraduate computer science majors in their third or fourth year of college.

About the Contributors

David J. Eck Ph.D. is a Professor at Department of Mathematics and Computer Science at the Hobart and William Smith Colleges.

Contribute to this Page

• Computer Fundamentals

Computer Graphics

Computer Network

• Interview Q

Graphic Systems

Input-output devices, scan conversion a line, scan conversion circle, scan converting ellipse, filled area primitives, 2d transformations, clipping techniques, pointing & positioning, 3d computer graphics, hidden surfaces.

• Send your Feedback to [email protected]

Help Others, Please Share

Learn Latest Tutorials

Transact-SQL

Reinforcement Learning

R Programming

React Native

Python Design Patterns

Python Pillow

Python Turtle

Preparation

Verbal Ability

Interview Questions

Company Questions

Trending Technologies

Artificial Intelligence

Cloud Computing

Data Science

Machine Learning

B.Tech / MCA

Data Structures

Operating System

Compiler Design

Computer Organization

Discrete Mathematics

Ethical Hacking

Software Engineering

Web Technology

Cyber Security

C Programming

Control System

Data Mining

Data Warehouse

• Random article
• Teaching guide
• Privacy & cookies

by Chris Woodford . Last updated: July 12, 2022.

Photo: Computer graphics allows us to "visualize" (imagine, mathematically) all sorts of things we can't (or won't ever) see. This image explores how the extreme gravity of two orbiting black holes distorts the light around them. Graphics by Jeremy Schnittman and Brian P. Powell, courtesy of NASA Goddard Space Flight Center .

What is computer graphics?

Photo: Oil paints like these can produce magical results in the right hands—but only in the right hands. Thankfully, those of us without the talent and skill to use them can still produce decent everyday art with computer graphics.

Raster and vector graphics

Photo: Raster graphics: This is a closeup of the paintbrushes in the photo of the artist's paint palette up above. At this magnification, you can clearly see the individual colored pixels (squares) from which the image is built, like bricks in a wall.

Photo: How a raster graphics program mirrors an image. Top: The pixels in the original image are represented by zeros and ones, with black pixels represented here by 1 and white ones represented by zero. That means the top image can be stored in the computer's memory as the binary number 100111. That's an example of a very small bitmap. Bottom: Now if you ask the computer to mirror the image, it simply reverses the order of the bits in the bitmap, left to right, giving the binary number 111001, which automatically reverses the original pattern of pixels. Other transformations of the picture, such as rotation and scaling, involve swapping the bits in more complex ways.

Photo: How anti-aliasing works. Pixelated images, like the word "pixelated" shown here, are made up of individual squares or dots, which are really easy for raster graphics displays (such as LCD computer screens) to draw. I copied this image directly from the italic word "pixelated" in the text up above. If you've not altered your screen colors, the original tiny text probably looks black and very smooth to your eyes. But in this magnified image, you'll see the letters are actually very jagged and made up of many colors. If you move back from your screen, or squint at the magnified word, you'll see the pixels and colors disappear back into a smooth black-and-white image. This is an example of anti-aliasing, a technique used to make pixelated words and other shapes smoother and easier for our eyes to process.

Vector graphics

Photo: Vector graphics: Drawing with Bézier curves ("paths") in the GIMP. You simply plot two points and then bend the line running between them however you want to create any curve you like.

3D graphics

Photo: NASA scientists think computer graphics will one day be so good that computer screens will replace the cockpit windows in airplanes . Instead of looking at a real view, the pilots will be shown a computerized image drawn from sensors that work at day or night in all weather conditions. For now, that remains a science fiction dream, because even well-drawn "3D" computer images like this are easy to tell from photographs of real-world scenes: they simply don't contain enough information to fool our amazingly fantastic eyes and brains. Photo courtesy of NASA Glenn .

What is computer graphics used for?

Photo: Computer graphics can save lives. Medical scan images are often complex computerized images built up from hundreds or thousands of detailed measurements of the human body or (as shown here) brain. Image by Govind Bhagavatheeshwaran and Daniel Reich courtesy of National Institutes of Health .

What is computer-aided design (CAD)?

Photo: Designing a plane? CAD makes it quicker and easier to transfer what's in your mind's eye into reality. Graphics by Ethan Baumann courtesy of NASA .

Graphics: CAD drawing of a hyper-X plane courtesy of NASA Langley Research Center (NASA-LaRC).

What is CAD used for?

Using cad in architecture.

Photo: Architectural models are traditionally made from paper or cardboard, but they're laborious and expensive to make, fragile and difficult to transport, and virtually impossible to modify. Computer models don't suffer from any of these drawbacks. Photo by Warren Gretz courtesy of US DOE/NREL .

Who invented computer graphics?

The beginnings.

• 1951: Jay Forrester and Robert Everett of Massachusetts Institute of Technology (MIT) produce Whirlwind , a mainframe computer that can display crude images on a television monitor or VDU (visual display unit).
• 1955: Directly descended from Whirlwind, MIT's SAGE (Semi-Automatic Ground Equipment) computer uses simple vector graphics to display radar images and becomes a key part of the US missile defense system.
• 1959: General Motors and IBM develop Design Augmented by Computers-1 (DAC-1), a CAD (computer-aided design) system to help engineers design cars.
• 1961: John Whitney, Sr. uses computer graphics to design a captivating title sequence for the Alfred Hitchcock thriller Vertigo .
• 1961: MIT student Steve Russell programs Spacewar! , the first graphical computer game, on a DEC PDP-1 minicomputer.
• 1963: Ivan Sutherland , a pioneer of human-computer interaction (making computers intuitively easy for humans to use), develops Sketchpad (also called Robot Draftsman), one of the first computer-aided design packages, in which images can be drawn on the screen using a lightpen (an electronic pen/stylus wired into the computer). Later, Sutherland develops virtual reality equipment and flight simulators.
• 1965: Howard Wise holds an exhibition of computer-drawn art at his pioneering gallery in Manhattan, New York.
• 1966: NASA's Jet Propulsion Laboratory (JPL) develops an image-processing program called VICAR (Video Image Communication and Retrieval) , running on IBM mainframes, to process images of the moon captured by spacecraft.
• 1970: Bézier curves are developed, soon becoming an indispensable tool in vector graphics.

Photo: A NASA scientist draws a graphic image on an IBM 2250 computer screen with a light pen. This was state-of-the-art technology in the 1970s! Photo by courtesy of NASA Ames Research Center (NASA-ARC) .

Computer graphics for everyone

Photo: Computer graphics, early 1980s style! Arcade games like Space Invaders were how most 40- and 50-something computer geeks first experienced computer graphics. At that time, even good computer screens could display only about 64,000 pixels—hence the relatively crudely drawn, pixelated graphics.

• 1972: Atari releases PONG , a popular version of ping-pong (table tennis) played by one or two players on a computer screen.
• 1973: Richard Shoup produces SuperPaint , a forerunner of modern computer graphic packages, at the Xerox PARC (Palto Alto Research Center) laboratory.
• 1970s: Ivan Sutherland's student Edwin Catmull becomes one of the pioneers of 3D computer-graphic animation, later playing key roles at Lucasfilm, Pixar, and Disney.
• 1981: UK company Quantel develops Paintbox , a revolutionary computer-graphic program that allows TV producers and filmakers to edit and manipulate video images digitally.
• 1982: The movie Tron , starring Jeff Bridges, mixes live action and computer graphic imagery in a story that takes a man deep inside a computer system.
• 1980s: The appearance of the affordable, easy-to-use Apple Macintosh computer paves the way for desktop publishing (designing things on your own small office computer) with popular computer graphic packages such as Aldus PageMaker (1985) and QuarkXPress (1987).
• 1985: Microsoft releases the first version of a basic raster-graphics drawing program called MS Paint . Thanks to its stripped-down simplicity, it becomes one of the world's most popular computer art programs.
• 1990: The first version of Adobe PhotoShop (one of the world's most popular professional graphic design packages) is released. A simple, affordable home graphics program called PaintShop (later PaintShop Pro) is launched the same year.
• 1993: University of Illinois student Marc Andreessen develops Mosaic , the first web browser to show text and images side-by-side, prompting a huge explosion in interest in the Web virtually overnight.
• 1995: Toy Story , produced by Pixar Animation Studios (founded by Apple's Steve Jobs, with Ed Catmull as its chief technology officer) demonstrates the impressive possibilities of CGI graphics in moviemaking. Stunning follow-up movies from the same stable include A Bug's Life , Monsters, Inc ., Finding Nemo , and The Incredibles .
• 1995: The GIMP (GNU Image Manipulation Program) is developed by University of California students Spencer Kimball and Peter Mattis as an open-source alternative to PhotoShop..
• 1999: The World Wide Web Consortium (W3C) begins development of SVG (Scalable Vector Graphics) , a way of using text-based (XML) files to provide higher-quality images on the Web. SVG images can include elements of both conventional vector and raster graphics.
• 2007: Apple launches its iPhone and iPod Touch products with touchscreen graphical user interfaces.
• 2017: Microsoft announces it will not kill off its basic but very popular Paint program, loved by computer artists for over 30 years.
• 2021: Facebook unveils plans for a new, interactive work and play space called the Metaverse. Will we live much of our future life in a computer-graphic virtual world?

If you liked this article...

Find out more, on this website.

• Computer models
• Computer mouse
• History of computers
• How to draw scientific artworks

On other websites

• ACM SIGGRAPH : Worldwide conference for computer graphics professionals held annually since 1974. (The acronym stands for the Association for Computing Machinery's Special Interest Group on Computer Graphics and Interactive Techniques.)
• Computer Graphics: Principles and Practice by John F. Hughes, Andries van Dam, et al. Addison-Wesley Professional, 2014. Classic introductory textbook, now in its third edition.
• Computer Graphics: Theory and Practice by Jonas Gomes et al. CRC Press, 2012. A relatively accessible up-to-date introduction, with less complex math than some of the other basic texts.
• Computer Graphics: Theory into Practice by Jeffrey J. McConnell. Jones and Bartlett, 2006. A much more theoretical and abstract approach to computer graphics that takes the human visual system as its starting point: if you know how our eyes work, you can produce more effective graphics.
• 3D Computer Graphics by Alan Watt. Addison-Wesley, 2000. Introduces 3D graphics for computer-aided design and the Web.
• AutoCAD for Dummies by Bill Fane. John Wiley & Sons, 2019. How to create and annotate technical drawings and models with AutoCAD.
• Computer Aided Design Guide for Architecture, Engineering and Construction by Ghassan Aouad, Angela Lee, and Song Wu. Spon, 2012. Very much an applied introducton to 2D, 3D, and 4D CAD.
• AutoCAD: Professional Tips and Techniques by Lynn Allen and Scott Onstott. Addison-Wesley, 2007. Hands-on introduction to one of the industry-standard CAD packages.
• Computer-Aided Design by Dean L. Taylor. Addison-Wesley, 1992. Quite old new, but still a comprehensive overview and a good introduction to the basic concepts.
• The Real Story of Pixar by Alvy Ray Smith, IEEE Spectrum, 3 Aug 2021. A look behind the scenes at the CGI pioneers.
• Chip Hall of Fame: Nvidia NV20 by Katherine Bourzac. IEEE Spectrum, 2 July 2018. Celebrating the classic graphics processor chip from 2001.
• Behold, the World's Most Famous Teapot by David C. Brock. IEEE Spectrum, 25 October 2017. How an exercise in mathematical modeling became a computer graphics icon.
• Microsoft Paint avoids brush with death by Zoe Kleinman. BBC News, 25 July 2017. The popular painting program gets a new lease of life.
• Motion Capture Technology Goes Into the Wild for Dawn of the Planet of the Apes by Tekla Perry. IEEE Spectrum, 10 July 2014. Motion capture helps computer graphic artists to draw movement more realistically by "tracking" the motion of a real, human actor. Here's how it worked on a typical movie, Dawn of Planet of the Apes.
• Yes, Your PC Can Generate Graphics This Stunning by Ryan Rigney, Wired, 25 September 2013. A review of Valley Benchmark and how it delivers realistic landscapes.
• GrabCAD Is Building Community in 3-D by Sarah Mitroff, Wired, 15 October 2012. A brief look at GrabCAD, a kind of Github for engineers and CAD designers.
• Creation Engine: Autodesk Wants to Help Anyone, Anywhere, Make Anything by Bob Parks, Wired, 21 September 2012. The latest computer-aided design software aims to predict how objects will behave after they leave the "virtual" computer screen and arrive in the real world.
• New Graphics Tech Promises Speed, Hyperrealism : Wired, April 22, 2010. New techniques make it easier to implement 3D graphics without extra hardware.

Text copyright © Chris Woodford 2010, 2022. All rights reserved. Full copyright notice and terms of use .

Rate this page

Tell your friends, cite this page, more to explore on our website....

• Get the book
• Send feedback

Cornell CIS Program of Computer Graphics

• Student Research Opportunities
• Graduate Program
• Educational Resources
• Images & Animation
• Community Resources
• Publications

What is Computer Graphics?

• History & Achievements

The field of computer graphics is a broad and diverse field that exists cross section between computer science and design. It is interested in the entire process of creating computer generated imagery, from creating digital three-dimensional models, to the process of texturing, rendering, and lighting those models, to the digital display of those renderings on a screen.

This process starts with simple object rendering techniques to transform mathematical representations of three-dimensional objects into a two-dimensional screen image, calculating projection transformations of vertices as well as occlusion and depth of objects. 

Detail and realism is added to these images through simulation of materials, textures, and lighting. The most accurate and realistic techniques involve understanding the way light interacts with objects in the physical world, and simulating those interactions as closely as possible on a computer. Phenomena such as reflections, transparencies, or diffuse lighting can be modeled using a variety of different algorithms, some designed to be physically accurate, others to be computationally efficient, depending on different needs. Virtual reality imagery must be generated in a matter of milliseconds, while a detailed architectural rendering may take hours of computation time.

With developments both in the hardware of GPUs and the software of rendering engines, Computer Graphics developments continue to push the bounds of both accuracy and speed of computer generated imagery.

A graphic is an image or visual representation of an object. Therefore, computer graphics are simply images displayed on a computer screen. Graphics are often contrasted with text, which is comprised of characters , such as numbers and letters, rather than images.

Computer graphics can be either two or three-dimensional. Early computers only supported 2D monochrome graphics, meaning they were black and white (or black and green, depending on the monitor ). Eventually, computers began to support color images. While the first machines only supported 16 or 256 colors, most computers can now display graphics in millions of colors.

2D graphics come in two flavors — raster and vector . Raster graphics are the most common and are used for digital photos, Web graphics, icons , and other types of images. They are composed of a simple grid of pixels , which can each be a different color. Vector graphics, on the other hand are made up of paths, which may be lines, shapes, letters, or other scalable objects. They are often used for creating logos, signs, and other types of drawings. Unlike raster graphics, vector graphics can be scaled to a larger size without losing quality.

3D graphics started to become popular in the 1990s, along with 3D rendering software such as CAD and 3D animation programs. By the year 2000, many video games had begun incorporating 3D graphics, since computers had enough processing power to support them. Now most computers now come with a 3D video card that handles all the 3D processing. This allows even basic home systems to support advanced 3D games and applications.

Test Your Knowledge

An asterisk (*) performs what mathematical function when used as an operator?

Tech Factor

Related terms.

• Raster Graphic
• Vector Graphic
• Color Depth

The Tech Terms Computer Dictionary

The definition of Graphics on this page is an original definition written by the TechTerms.com team . If you would like to reference this page or cite this definition, please use the green citation links above.

The goal of TechTerms.com is to explain computer terminology in a way that is easy to understand. We strive for simplicity and accuracy with every definition we publish. If you have feedback about this definition or would like to suggest a new technical term, please contact us .

Sign up for the free TechTerms Newsletter

You can unsubscribe or change your frequency setting at any time using the links available in each email. Questions? Please contact us .

We just sent you an email to confirm your email address. Once you confirm your address, you will begin to receive the newsletter.

If you have any questions, please contact us .

Help | Advanced Search

Computer Science > Graphics

Title: fsh: 3d representation via fibonacci spherical harmonics.

Abstract: Spherical harmonics are a favorable technique for 3D representation, employing a frequency-based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non-uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band-limited function. This provides a novel solution for spherical harmonic transformation on non-equiangular grids. The key advantages of our FSH method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32-degree spherical harmonic coefficients is reduced by approximately 34.6\% for SFG compared to equiangular grids; and 3) FSH offers more stable frequency domain representations, especially for rotating functions. FSH enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

AI Image Generator

Generate an image using Generative AI by describing what you want to see, all images are published publicly by default.

Get inspiration, text to image prompts and trending image generations; just need to login first!

Do you have a question.

McKinsey Technology Trends Outlook 2023

After a tumultuous 2022 for technology investment and talent, the first half of 2023 has seen a resurgence of enthusiasm about technology’s potential to catalyze progress in business and society. Generative AI deserves much of the credit for ushering in this revival, but it stands as just one of many advances on the horizon that could drive sustainable, inclusive growth and solve complex global challenges.

To help executives track the latest developments, the McKinsey Technology Council  has once again identified and interpreted the most significant technology trends unfolding today. While many trends are in the early stages of adoption and scale, executives can use this research to plan ahead by developing an understanding of potential use cases and pinpointing the critical skills needed as they hire or upskill talent to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, and investment to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into underlying technologies, uncertainties, and questions surrounding each trend. This year, we added an important new dimension for analysis—talent. We provide data on talent supply-and-demand dynamics for the roles of most relevance to each trend. (For more, please see the sidebar, “Research methodology.”)

New and notable

All of last year’s 14 trends remain on our list, though some experienced accelerating momentum and investment, while others saw a downshift. One new trend, generative AI, made a loud entrance and has already shown potential for transformative business impact.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend. Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents.
• Research. Data on research publications are sourced from the Lens (www.lens.org).
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data is drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s study to reflect the evolution of technology trends:

• The generative-AI trend was added since last year’s study.
• We adjusted the definitions of electrification and renewables (previously called future of clean energy) and climate technologies beyond electrification and renewables (previously called future of sustainable consumption).
• Data sources were updated. This year, we included only closed deals in PitchBook data, which revised downward the investment numbers for 2018–22. For future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

This new entrant represents the next frontier of AI. Building upon existing technologies such as applied AI and industrializing machine learning, generative AI has high potential and applicability across most industries. Interest in the topic (as gauged by news and internet searches) increased threefold from 2021 to 2022. As we recently wrote, generative AI and other foundational models  change the AI game by taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users. Generative AI is poised to add as much as $4.4 trillion in economic value from a combination of specific use cases and more diffuse uses—such as assisting with email drafts—that increase productivity. Still, while generative AI can unlock significant value, firms should not underestimate the economic significance and the growth potential that underlying AI technologies and industrializing machine learning can bring to various industries.

Investment in most tech trends tightened year over year, but the potential for future growth remains high, as further indicated by the recent rebound in tech valuations. Indeed, absolute investments remained strong in 2022, at more than $1 trillion combined, indicating great faith in the value potential of these trends. Trust architectures and digital identity grew the most out of last year’s 14 trends, increasing by nearly 50 percent as security, privacy, and resilience become increasingly critical across industries. Investment in other trends—such as applied AI, advanced connectivity, and cloud and edge computing—declined, but that is likely due, at least in part, to their maturity. More mature technologies can be more sensitive to short-term budget dynamics than more nascent technologies with longer investment time horizons, such as climate and mobility technologies. Also, as some technologies become more profitable, they can often scale further with lower marginal investment. Given that these technologies have applications in most industries, we have little doubt that mainstream adoption will continue to grow.

Organizations shouldn’t focus too heavily on the trends that are garnering the most attention. By focusing on only the most hyped trends, they may miss out on the significant value potential of other technologies and hinder the chance for purposeful capability building. Instead, companies seeking longer-term growth should focus on a portfolio-oriented investment across the tech trends most important to their business. Technologies such as cloud and edge computing and the future of bioengineering have shown steady increases in innovation and continue to have expanded use cases across industries. In fact, more than 400 edge use cases across various industries have been identified, and edge computing is projected to win double-digit growth globally over the next five years. Additionally, nascent technologies, such as quantum, continue to evolve and show significant potential for value creation. Our updated analysis for 2023 shows that the four industries likely to see the earliest economic impact from quantum computing—automotive, chemicals, financial services, and life sciences—stand to potentially gain up to $1.3 trillion in value by 2035. By carefully assessing the evolving landscape and considering a balanced approach, businesses can capitalize on both established and emerging technologies to propel innovation and achieve sustainable growth.

Tech talent dynamics

We can’t overstate the importance of talent as a key source in developing a competitive edge. A lack of talent is a top issue constraining growth. There’s a wide gap between the demand for people with the skills needed to capture value from the tech trends and available talent: our survey of 3.5 million job postings in these tech trends found that many of the skills in greatest demand have less than half as many qualified practitioners per posting as the global average. Companies should be on top of the talent market, ready to respond to notable shifts and to deliver a strong value proposition to the technologists they hope to hire and retain. For instance, recent layoffs in the tech sector may present a silver lining for other industries that have struggled to win the attention of attractive candidates and retain senior tech talent. In addition, some of these technologies will accelerate the pace of workforce transformation. In the coming decade, 20 to 30 percent of the time that workers spend on the job could be transformed by automation technologies, leading to significant shifts in the skills required to be successful. And companies should continue to look at how they can adjust roles or upskill individuals to meet their tailored job requirements. Job postings in fields related to tech trends grew at a very healthy 15 percent between 2021 and 2022, even though global job postings overall decreased by 13 percent. Applied AI and next-generation software development together posted nearly one million jobs between 2018 and 2022. Next-generation software development saw the most significant growth in number of jobs (exhibit).

Image description:

Small multiples of 15 slope charts show the number of job postings in different fields related to tech trends from 2021 to 2022. Overall growth of all fields combined was about 400,000 jobs, with applied AI having the most job postings in 2022 and experiencing a 6% increase from 2021. Next-generation software development had the second-highest number of job postings in 2022 and had 29% growth from 2021. Other categories shown, from most job postings to least in 2022, are as follows: cloud and edge computing, trust architecture and digital identity, future of mobility, electrification and renewables, climate tech beyond electrification and renewables, advanced connectivity, immersive-reality technologies, industrializing machine learning, Web3, future of bioengineering, future of space technologies, generative AI, and quantum technologies.

End of image description.

This bright outlook for practitioners in most fields highlights the challenge facing employers who are struggling to find enough talent to keep up with their demands. The shortage of qualified talent has been a persistent limiting factor in the growth of many high-tech fields, including AI, quantum technologies, space technologies, and electrification and renewables. The talent crunch is particularly pronounced for trends such as cloud computing and industrializing machine learning, which are required across most industries. It’s also a major challenge in areas that employ highly specialized professionals, such as the future of mobility and quantum computing (see interactive).

Michael Chui is a McKinsey Global Institute partner in McKinsey’s Bay Area office, where Mena Issler is an associate partner, Roger Roberts  is a partner, and Lareina Yee  is a senior partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Bharat Bahl, Soumya Banerjee, Arjita Bhan, Tanmay Bhatnagar, Jim Boehm, Andreas Breiter, Tom Brennan, Ryan Brukardt, Kevin Buehler, Zina Cole, Santiago Comella-Dorda, Brian Constantine, Daniela Cuneo, Wendy Cyffka, Chris Daehnick, Ian De Bode, Andrea Del Miglio, Jonathan DePrizio, Ivan Dyakonov, Torgyn Erland, Robin Giesbrecht, Carlo Giovine, Liz Grennan, Ferry Grijpink, Harsh Gupta, Martin Harrysson, David Harvey, Kersten Heineke, Matt Higginson, Alharith Hussin, Tore Johnston, Philipp Kampshoff, Hamza Khan, Nayur Khan, Naomi Kim, Jesse Klempner, Kelly Kochanski, Matej Macak, Stephanie Madner, Aishwarya Mohapatra, Timo Möller, Matt Mrozek, Evan Nazareth, Peter Noteboom, Anna Orthofer, Katherine Ottenbreit, Eric Parsonnet, Mark Patel, Bruce Philp, Fabian Queder, Robin Riedel, Tanya Rodchenko, Lucy Shenton, Henning Soller, Naveen Srikakulam, Shivam Srivastava, Bhargs Srivathsan, Erika Stanzl, Brooke Stokes, Malin Strandell-Jansson, Daniel Wallance, Allen Weinberg, Olivia White, Martin Wrulich, Perez Yeptho, Matija Zesko, Felix Ziegler, and Delphine Zurkiya.

They also wish to thank the external members of the McKinsey Technology Council.

This interactive was designed, developed, and edited by McKinsey Global Publishing’s Nayomi Chibana, Victor Cuevas, Richard Johnson, Stephanie Jones, Stephen Landau, LaShon Malone, Kanika Punwani, Katie Shearer, Rick Tetzeli, Sneha Vats, and Jessica Wang.

Explore a career with us

Related articles.

McKinsey Technology Trends Outlook 2022

Value creation in the metaverse

Quantum computing funding remains strong, but talent gap raises concern

• Engineering Mathematics
• Discrete Mathematics
• Operating System
• Computer Networks
• Digital Logic and Design
• C Programming
• Data Structures
• Theory of Computation
• Compiler Design
• Computer Org and Architecture
• Share Your Experience
• Computer Graphics
• Basic Graphic Programming in C++
• Vector vs Raster Graphics
• Segments in Computer Graphics
• Image Formats

Output Primitives

• DDA Line generation Algorithm in Computer Graphics
• Bresenham’s Line Generation Algorithm
• Mid-Point Line Generation Algorithm
• Program to find line passing through 2 Points
• Bresenham’s circle drawing algorithm
• Anti-aliased Line | Xiaolin Wu's algorithm
• Neighbors of a point on a circle using Bresenham's algorithm
• Mid-Point Circle Drawing Algorithm
• Boundary Fill Algorithm
• Flood fill Algorithm - how to implement fill() in paint?
• Flood fill algorithm using C graphics
• Draw a line in C++ graphics
• Draw Rectangle in C graphics
• Draw circle in C graphics
• Draw a circle without floating point arithmetic
• Code to Generate the Map of India (With Explanation)

2-Dimensional Viewing

• 2D Transformation in Computer Graphics | Set 1 (Scaling of Objects)
• 2D Transformation | Rotation of objects
• Point Clipping Algorithm in Computer Graphics
• Line Clipping | Set 1 (Cohen–Sutherland Algorithm)
• Polygon Clipping | Sutherland–Hodgman Algorithm
• Implementation of a Falling Matrix

Visible Surface Detection

• A-Buffer Method
• Z-Buffer or Depth-Buffer method
• Back-Face Detection Method

3-Dimension Object Representation

• Snowflakes Fractal using Python
• Koch Curve or Koch Snowflake
• Klee's Algorithm (Length Of Union Of Segments of a line)
• Cubic Bezier Curve Implementation in C
• Fractals in C/C++
• Scan-line Polygon filling using OPENGL in C
• Rendering a Triangle using OpenGL(using Shaders)
• Getting started with OpenGL
• OpenGL program for Simple Ball Game
• OpenGL program for simple Animation (Revolution) in C
• Translation of objects in computer graphics

Graphics function in C

• pieslice() function in C
• outtextxy() function in C
• settextstyle function in C
• outtext() function in C
• setlinestyle() function in C
• getx() function in C
• sector() function in C
• moveto() function in C
• gety() function in C
• getmaxx() function in C
• lineto() function in C
• arc function in C
• bar3d() function in C graphics
• moverel() function in C
• cleardevice() function in C
• closegraph() function in C
• drawpoly() function in C
• putpixel() function in C
• getarcoords() function in C
• getbkcolor() function in C
• getmaxcolor() function in C
• getpixel() function in C
• setcolor function in C
• imagesize() function in C
• textheight() function in C
• textwidth() function in C
• grapherrormsg() function in C
• fillpoly() function in C
• fillellipse() function in C
• bar() function in C graphics
• How to add "graphics.h" C/C++ library to gcc compiler in Linux
• How to include graphics.h in CodeBlocks?
• Computer Graphics |Cathode Ray Oscilloscope| Cathode ray tube (video display technology)
• High Definition Multimedia Interface (HDMI)
• Common Video Format
• Audio Format
• Coding for Everyone Course

Recent Articles on Computer Graphics

Table of Content

Output Primitives :

2-dimensional viewing :, visible surface detection :, 3-dimension object representation :, graphics function in c :.

• Anti-aliased Line | Xiaolin Wu’s algorithm
• Neighbors of a point on a circle using Bresenham’s algorithm
• Flood fill Algorithm – how to implement fill() in paint?
• Draw ellipse in C graphics
• Code to generate the map of India (with explanation)
• Klee’s Algorithm (Length Of Union Of Segments of a line)
• getmaxy() function in C
• linerel() function in C
• setfillstyle() and floodfill() in C
• bar3d() function in C
• bar() function in C
• How to add “graphics.h” C/C++ library to gcc compiler in Linux
• Cathode ray tube (video display device)

Please Login to comment...

Similar reads.

• OpenAI launches ChatGPT Edu for universities: Here is what it is and how it works
• NVIDIA Launches AI Assistants With GeForce RTX AI PCs
• How to Download WhatsApp Status in iPhone
• 10 Best ChatGPT Prompts for Increasing Productivity in 2024
• 10 Best Free Reverse Phone Number Lookup

Improve your Coding Skills with Practice

What kind of Experience do you want to share?