Rate-Distortion-Complexity Optimized Framework for Multi-Model Image Compression

Abstract

Learned Image Compression (LIC) has experienced rapid growth with the emergence of diverse frameworks. However, the variability in model design and training datasets poses a challenge for the universal application of a single coding model. To address this problem, this paper introduces a pioneering multi-model image coding framework that integrates various image codecs to overcome these limitations. By dynamically allocating codecs to different image regions, our framework optimizes reconstruction quality within the constraints of limited bitrate and decoding time, offering a high-performance, ubiquitous solution for the rate-distortion-complexity trade-off. Our framework features a detailed codec assignment algorithm based on the Simulated Annealing (SA) method, selected for its proven efficacy in managing the discrete and intricate nature of codec assignment optimization. We have implemented a coarse-to-fine strategy, which significantly enhances efficiency. Notably, our framework maintains compatibility with all standard image codecs without necessitating structural modifications. Empirical results indicate that our framework establishes a new standard in LIC, advancing the Pareto frontier for performance-complexity trade-offs. It achieves a significant 70% reduction in decoding time compared to current state-of-the-art methods, without compromising reconstruction quality. Furthermore, under comparable conditions, our approach not only outperforms but significantly eclipses existing Rate-Distortion-Complexity (RDC) optimized codecs, with decoding speeds up to 30 times faster.