The Rate-Distortion-Perception Trade-Off with Algorithmic Realism
Yassine Hamdi, Aaron B. Wagner, Deniz G\"und\"uz
TL;DR
This paper explores the trade-off between rate, distortion, and perceptual realism in lossy image compression, demonstrating that realism constraints can be met without common randomness for practical batch sizes.
Contribution
It introduces a realism constraint based on a universal critic and shows the optimal rate-distortion trade-off can be achieved without common randomness in practical scenarios.
Findings
Realism constraints can be satisfied without common randomness for practical batch sizes.
The optimal rate-distortion trade-off under realism constraints is characterized and asymptotically achievable.
High-rate common randomness is not necessary in practice for achieving realism constraints.
Abstract
Realism constraints (or constraints on perceptual quality) have received considerable recent attention within the context of lossy compression, particularly of images. Theoretical studies of lossy compression indicate that high-rate common randomness between the compressor and the decompressor is a valuable resource for achieving realism. On the other hand, the utility of significant amounts of common randomness has not been noted in practice. We offer an explanation for this discrepancy by considering a realism constraint that requires satisfying a universal critic that inspects realizations of individual compressed reconstructions, or batches thereof. We characterize the optimal rate-distortion trade-off under such a realism constraint, and show that it is asymptotically achievable without any common randomness, unless the batch size is impractically large.
Peer Reviews
Decision·Submitted to ICLR 2025
* The paper provides a novel perspective on the rate-distortion-perception tradeoff by adopting the concept of universal critics. * The paper presents rigorous theoretical analysis and proofs to support its claims. * The theoretical finding that near-perfect realism is achievable without common randomness has significant practical implications for lossy compression.
While the paper presents a novel and potentially impactful contribution, its clarity and accessibility are hindered by a dense presentation style. The heavy use of technical notation and the lack of illustrative examples make it challenging to grasp the core concepts and implications of the proposed framework. Specifically, the paper would benefit from: * More explanatory discussions: For instance, a concise discussion following Definition 3.3 would clarify the meaning and significance of the
1. Interesting Insight into Realism Constraints: By redefining perceptual realism through an algorithmic lens, the paper provides a fresh perspective on the RDP trade-off and its practical applications in lossy compression. 2. Reduced Dependency on Common Randomness: The finding that common randomness is only needed in impractically large batches addresses a significant gap in previous theoretical predictions versus experimental observations. 3. Good Theoretical Foundation: The study provides ri
While the paper provides rigorous theoretical derivations and proofs, one significant limitation is the lack of practical illustrations or implementations that could help readers appreciate the impact and contributions of the proposed framework in real-world applications. The authors claim that algorithmic realism simplifies the practical attainment of the rate-distortion-perception (RDP) trade-off by reducing the dependency on common randomness between encoder and decoder. However, without prac
It is great to have a RDP which is achievable without randomness. Afterall, the human eye distinguishs images in a per-image setting without randomness. The proposed RDP is better aligned to human perception in this sense. I have not went through the details of proofs due to the complex notation. However, I am in general glad to see a new RDP function with achievability & converse, zero-shot & asymptotic.
The reason why I am not willing to give this paper a higher rating is that the authors have not shown how the proposed RDP can guide perceptual compression / super-resolution, not even a toy example. The RDP function in [Blau & Michaeli 2019] has many disadvantages, which this paper does not have: * [Blau & Michaeli 2019] does not prove the converse. * [Blau & Michaeli 2019] does not distinguish zero-shot and asymptotic function. Those issues have not been fixed until [A coding theorem for the
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Taxonomy
TopicsAdvanced Data Compression Techniques · Image and Video Quality Assessment · Wireless Communication Security Techniques