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[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML’19 long oral)

This paper mathematically proves that optimizing for bit-rate and distortion when compressing perceptual data such as images, video and audio is not the right thing to do. Optimizing for bit-rate and distortion leads to unnatural low-quality outputs, as perceived by humans. Instead, compression algorithms should directly optimize for perceptual quality, yet this will always come at the cost of increased rate or distortion. An important implication is that comparing compression algorithms by rate-distortion curves, as commonly done, is misleading.

Link to PDF: http://proceedings.mlr.press/v97/blau19a/blau19a.pdf

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.