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arXiv · 2502.10628

On Self-Adaptive Perception Loss Function for Sequential Lossy Compression

Sadaf Salehkalaibar·Buu Phan·Likun Cai·Joao Atz Dick·Wei Yu·Jun Chen·Ashish Khisti
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Abstract

We consider causal, low-latency, sequential lossy compression, with mean squared-error (MSE) as the distortion loss, and a perception loss function (PLF) to enhance the realism of reconstructions. As the main contribution, we propose and analyze a new PLF that considers the joint distribution between the current source frame and the previous reconstructions. We establish the theoretical rate-distortion-perception function for first-order Markov sources and analyze the Gaussian model in detail. From a qualitative perspective, the proposed metric can simultaneously avoid the error-permanence phenomenon and also better exploit the temporal correlation between high-quality reconstructions. The proposed metric is referred to as self-adaptive perception loss function (PLF-SA), as its behavior adapts to the quality of reconstructed frames. We provide a detailed comparison of the proposed perception loss function with previous approaches through both information theoretic analysis as well as experiments involving moving MNIST and UVG datasets.

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Sadaf Salehkalaibar, Buu Phan, Likun Cai, Joao Atz Dick, Wei Yu, Jun Chen, Ashish Khisti. 2025-02-15. On Self-Adaptive Perception Loss Function for Sequential Lossy Compression. https://arxiv.org/abs/2502.10628

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