GST
Abstract
Modern GPUs supporting compressed textures allow interactive application developers to save scarce GPU resources such as VRAM and bandwidth. Compressed textures use fixed compression ratios whose lossy representations are significantly poorer quality than traditional image compression formats such as JPEG. We present a new method in the class of supercompressed textures that provides an additional layer of compression to already compressed textures. Our texture representation is designed for endpoint compressed formats such as DXT and PVRTC and decoding on commodity GPUs. We apply our algorithm to commonly used formats by separating their representation into two parts that are processed independently and then entropy encoded. Our method preserves the CPU-GPU bandwidth during the decoding phase and exploits the parallelism of GPUs to provide up to 3X faster decode compared to prior texture supercompression algorithms. Along with the gains in decoding speed, our method maintains both the compression size and quality of current state of the art texture representations.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 11, 2016
- Source ID
- 10.1145/2980179.2982439
Entities
People
- Dinesh Manocha
- Pavel Krajcevski
- Srihari Pratapa
Organizations
- Army Research Office
- Samsung Group
- University of North Carolina at Chapel Hill