• Announcements

    • khawk

      Download the Game Design and Indie Game Marketing Freebook   07/19/17

      GameDev.net and CRC Press have teamed up to bring a free ebook of content curated from top titles published by CRC Press. The freebook, Practices of Game Design & Indie Game Marketing, includes chapters from The Art of Game Design: A Book of Lenses, A Practical Guide to Indie Game Marketing, and An Architectural Approach to Level Design. The GameDev.net FreeBook is relevant to game designers, developers, and those interested in learning more about the challenges in game development. We know game development can be a tough discipline and business, so we picked several chapters from CRC Press titles that we thought would be of interest to you, the GameDev.net audience, in your journey to design, develop, and market your next game. The free ebook is available through CRC Press by clicking here. The Curated Books The Art of Game Design: A Book of Lenses, Second Edition, by Jesse Schell Presents 100+ sets of questions, or different lenses, for viewing a game’s design, encompassing diverse fields such as psychology, architecture, music, film, software engineering, theme park design, mathematics, anthropology, and more. Written by one of the world's top game designers, this book describes the deepest and most fundamental principles of game design, demonstrating how tactics used in board, card, and athletic games also work in video games. It provides practical instruction on creating world-class games that will be played again and again. View it here. A Practical Guide to Indie Game Marketing, by Joel Dreskin Marketing is an essential but too frequently overlooked or minimized component of the release plan for indie games. A Practical Guide to Indie Game Marketing provides you with the tools needed to build visibility and sell your indie games. With special focus on those developers with small budgets and limited staff and resources, this book is packed with tangible recommendations and techniques that you can put to use immediately. As a seasoned professional of the indie game arena, author Joel Dreskin gives you insight into practical, real-world experiences of marketing numerous successful games and also provides stories of the failures. View it here. An Architectural Approach to Level Design This is one of the first books to integrate architectural and spatial design theory with the field of level design. The book presents architectural techniques and theories for level designers to use in their own work. It connects architecture and level design in different ways that address the practical elements of how designers construct space and the experiential elements of how and why humans interact with this space. Throughout the text, readers learn skills for spatial layout, evoking emotion through gamespaces, and creating better levels through architectural theory. View it here. Learn more and download the ebook by clicking here. Did you know? GameDev.net and CRC Press also recently teamed up to bring GDNet+ Members up to a 20% discount on all CRC Press books. Learn more about this and other benefits here.
Sign in to follow this  
Followers 0
Inferiarum

Compressed Sensing for Voxel Data Compression

4 posts in this topic

Hello guys,

 

Has anyone tried to compress voxel data with compressed sensing? It seems to be a suitable application. If you for example consider only voxels on the surface of an object to be important the whole data should be quite sparse.

 

Well, just an idea if someone wants to try it :)

 

0

Share this post


Link to post
Share on other sites
Compressed sensing is not meant to be a compression technique: The whole point is using compression techniques to interpolate from sparse data, but you need to start with a compression technique.
0

Share this post


Link to post
Share on other sites

I get what you're going for: You throw a huge set of basis functions at your data; you find a representation in terms of a small number of them; and you send the coefficients.

 

From one angle, this is the same reason all the Fourier image compression techniques work (JPEG and its descendents): It happens that, with this choice of basis, most of your image energy gets concentrated in a small number of coefficients (in this case, even without doing anything to explicitly encourage sparsity).  So why not try other bases and actually reward sparsity with L1 regularization?  It sounds plausible, right?

 

Unfortunately, other people have had the same idea, and as far as I know, none of them have actually been able to achieve competitive compression of e.g. images in this manner.

 

So if this is for your job and you need results soon, I might encourage you to look elsewhere.  But if this is for research or a hobby project -- then who knows?  Go grab a convex programming solver and see if you get anywhere.

0

Share this post


Link to post
Share on other sites

Compressed sensing is not meant to be a compression technique: The whole point is using compression techniques to interpolate from sparse data, but you need to start with a compression technique.

 

I think a lot of people conflate "compressed sensing" with L1 regularization...'

 

Taking a step back from L1 regularization, and thinking just about a choice of basis: I wonder how far OP'd get with splitting the grid up into blocks and doing a Hadamard transform of each.  It's more-or-less the direct generalization of JPEG to the "3d binary pixels" case...

1

Share this post


Link to post
Share on other sites

To answer my own question: When you throw out the small coefficients of the Hadamard transform in 1d, what you get looks awful (see attached image).

0

Share this post


Link to post
Share on other sites

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Sign up for a new account in our community. It's easy!


Register a new account

Sign in

Already have an account? Sign in here.


Sign In Now
Sign in to follow this  
Followers 0