Problem
Research questions and friction points this paper is trying to address.
successive refinement
strong-sense perfect perception
lossy source coding
Successive Refinement Under Strong-Sense Perfect Perception
🤖 AI Summary
本文解决了在强感知约束下的多终端有损信源编码问题，通过分析感知质量的影响，并采用输出受限的有损信源编码方法，证明了即使在强感知约束下Bernoulli源也是可逐步细化的。
📝 Abstract
We revisit a multiterminal lossy source coding problem named successive refinement and derive the rate-distortion-perception region under the strong-sense perfect perception constraint in the presence of unlimited common randomness. Specifically, in successive refinement, one aims to compress a source sequence and allows two distinct decoders to recover the source sequence at different distortion levels. By imposing the strong-sense perfect perception constraint, our results refine the previous result by analyzing the impact of the perceptual quality. Our achievability proof is inspired by output constrained lossy source coding and our converse proof adapts the proof steps of the standard successive refinement problem. Furthermore, we provide a numerical example of the Bernoulli source to illustrate our result and show that the Bernoulli source under Hamming distortion is successively refinable even with the strong-sense perfect perception constraint.
Innovation
Methods, ideas, or system contributions that make the work stand out.
successive refinement
strong-sense perfect perception
rate-distortion-perception region
unlimited common randomness
Bernoulli source
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