arXiv:2604.14603v2 Announce Type: replace-cross Abstract: The fundamental limit of natural signal compression has traditionally been characterized by classical rate-distortion (RD) theory through the tradeoff between coding rate and reconstruction distortion, while the rate-distortion-perception (RDP) framework introduces a divergence-based measure of perceptual quality as a modeling principle, leaving its theoretical origin unclear. In this paper, motivated by a synonymity-based semantic information perspective, we reformulate perceptual reconstruction as recovering any admissible sample with
The paper, published on arXiv, represents ongoing academic research into the theoretical underpinnings of AI compression and perception, building on existing frameworks like rate-distortion theory.
It refines the theoretical understanding of perceptual quality in AI models, which is crucial for advancing efficient and human-aligned AI systems.
The conceptual framework for evaluating AI model efficiency and perceptual accuracy is being updated, potentially leading to more sophisticated compression and generation techniques.
- · AI researchers
- · Machine learning engineers
- · Generative AI developers
- · Previous suboptimal compression methods
- · AI models without perceptual optimization
Improved efficiency and quality in AI-driven data compression and generative models.
More sophisticated and human-perceivable AI outputs across various applications, from media to autonomous systems.
Reduced computational resource demands for high-quality AI, impacting areas like energy consumption and hardware design.
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Read at arXiv cs.LG