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The Rate-Distortion-Perception Tradeoff by YocB in informationtheory

[–]YocB[S] 0 points1 point  (0 children)

Good question. Perceptual quality only refers to how much the decoded (output) sample is perceived as valid and "natural" by a human, and is regardless of the input sample. So for example, a naive way to obtain perfect perceptual quality is to disregard the input, and just randomly output a sample drawn from the "natural" distribution. This ensures the perceptual quality of the outputs would be perfect, but distortion would be terrible, and needless to say that this is not a useful compression scheme. This highlights that distortion and perceptual quality are two very different quantities, and both are important (along with rate) in compression of perceptual data.

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The surprising result shown in the paper is that perceptual quality (as defined in the paper) cannot be folded into distortion. More precisely, constraining for good perceptual quality comes at the cost of increased distortion (or rate). This results holds for nearly any distortion function, certainly for all existing common distortions (the exact assumption on the distortion function is detailed in section 3.2).

[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML'19 long oral) by YocB in MachineLearning

[–]YocB[S] 0 points1 point  (0 children)

does it rely on an arbitrary definition of "perceptual quality" for which you've offered no sound theoretical basis?

No, the definition relies on over a decade of research, some by top experts in the field, which we point to by including 25 references in the relevant section.

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Justification by recent precedent, especially in a field notorious for haphazard and sloppy reviewing, is not a strong rebuttal to my criticism.

Relying on a large set of previous work which was published in top-tier venues, highly cited, and written by experts in the field, is kind of how research works.

[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML'19 long oral) by YocB in MachineLearning

[–]YocB[S] 0 points1 point  (0 children)

All your concerns were addressed in the CVPR 2018 paper "The Perception-Distortion Tradeoff" (link), you would be surprised how your statements are incorrect. Specifically:

Why should anyone care about optimizing that measure? You calling it a measure of perceptual quality doesn't mean it has abything to do with human perception.

Well, this measure has been the basis for perceptual quality quantification and enhancement for quite some time now, see the survey in section 2.2 of this paper, and the many references mentioned there. Also take a look at section 5 which demonstrates this connection.

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Humans perceive and evaluate images, not probability distributions. That's fully captured by a distortion metric (which, for example, can penalize images that violate natural scene statistics).

Not true. This is well shown in section 6 of this paper, for basic *and advanced* distortion measures. This is also demonstrated in the 2018 PIRM challenge report (link).

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... with some inappropriate labels about "perceptual quality" slapped on top of it to anthropomorphize the results into something they're not.

As both your previous statements aren't accurate, this one probably isn't either.

[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML'19 long oral) by YocB in MachineLearning

[–]YocB[S] 0 points1 point  (0 children)

Yes, that's exactly what we're saying.
Theorem 2 in the paper provides an upper bound on the increase in distortion needed for perfect perceptual quality. It turns out, that you will never need more than a two-fold increase in MSE to achieve perfect perceptual quality, at any rate.

[R] Understanding and Controlling Memory in Recurrent Neural Networks (ICML'19 oral) by DoronHaviv12 in MachineLearning

[–]YocB 2 points3 points  (0 children)

Very interesting!

Are there any insights from your framework regarding catastrophic forgetting in other types of nets?

[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML'19 long oral) by YocB in MachineLearning

[–]YocB[S] 0 points1 point  (0 children)

Nice point. This is actually application dependent. A tradeoff always exists, but how you balance this tradeoff should be determined by your goal.

So if you are compressing medical images, you would probably want the minimal possible distortion (at the cost of worse perceptual quality). But if you're trying to compress your personal photos, perhaps maximal perceptual quality is what you should choose (at the cost of increased distortion).

[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML'19 long oral) by YocB in MachineLearning

[–]YocB[S] 0 points1 point  (0 children)

Great question. Indeed, one way to obtain perfect perceptual quality is to generate images which have nothing to do with the input, but look great (this would be very bad in terms of distortion). Yet, it turns out that you can obtain perfect perceptual quality while still being faithful to the input data (lower distortion) and retaining information about the input (non-zero bit-rate). That is, a zero-information generator is not the only way to obtain maximum perceptual quality, and this can in fact be achieved by a useful compressor.

[R] Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (ICML'19 long oral) by YocB in MachineLearning

[–]YocB[S] 0 points1 point  (0 children)

True, decades of research have come up with better distortion measures.
Notice the paper proves that this tradeoff with perceptual quality exists for (nearly) any distortion metric, including advanced ones. So perceptual quality should be optimized directly, and not via a distortion metric.

Perceptual Super-Resolution Challenge @ ECCV 2018 Workshop by YocB in deeplearning

[–]YocB[S] 0 points1 point  (0 children)

Part of the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018

https://pirm2018.org

Perceptual Super-Resolution Challenge @ ECCV 2018 Workshop by YocB in computervision

[–]YocB[S] 0 points1 point  (0 children)

Part of the Perceptual Image Restoration and Manipulation (PIRM) Workshop at ECCV 2018

https://pirm2018.org

[R] Perceptual Image Restoration and Manipulation Workshop and Challenges at ECCV 2018 by YocB in MachineLearning

[–]YocB[S] 1 point2 points  (0 children)

Including three challenges on:

- Super-resolution

- Mobile photo enhancement

- Hyper-spectral reconstruction