Exploiting Human Color Discrimination for Memory- and Energy-Efficient Image Encoding in Virtual Reality
Nisarg Ujjainkar, Ethan Shahan, Kenneth Chen, Budmonde Duinkharjav, Qi, Sun, Yuhao Zhu

TL;DR
This paper introduces a perceptually lossless image compression system for VR that leverages human color discrimination to significantly reduce DRAM traffic and energy consumption without perceptible quality loss.
Contribution
It presents a novel color adjustment algorithm based on psychophysical insights, coupled with lightweight hardware support, to improve framebuffer compression in VR systems.
Findings
Reduces DRAM traffic by 66.9% in VR systems.
Outperforms existing framebuffer compression by up to 20.4%.
Maintains perceptual fidelity with minimal quality degradation.
Abstract
Virtual Reality (VR) has the potential of becoming the next ubiquitous computing platform. Continued progress in the burgeoning field of VR depends critically on an efficient computing substrate. In particular, DRAM access energy is known to contribute to a significant portion of system energy. Today's framebuffer compression system alleviates the DRAM traffic by using a numerically lossless compression algorithm. Being numerically lossless, however, is unnecessary to preserve perceptual quality for humans. This paper proposes a perceptually lossless, but numerically lossy, system to compress DRAM traffic. Our idea builds on top of long-established psychophysical studies that show that humans cannot discriminate colors that are close to each other. The discrimination ability becomes even weaker (i.e., more colors are perceptually indistinguishable) in our peripheral vision. Leveraging…
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Taxonomy
TopicsImage and Video Quality Assessment · Visual Attention and Saliency Detection · Image Enhancement Techniques
