Rate-distortion optimal evolutionary algorithm for JPEG quantization with multiple rates

The JPEG standard is used extensively in image-related applications. The optimization on a JPEGstandard compressor is of high importance for image storage and transmission. In this paper, we propose a versatile and efficient quantization optimization method for image compression. It is based on multi-objective optimization and is JPEG standard-compatible. Unlike the existing optimization methods, our method fully considers the rate-distortion optimal principle and provides several optimal solutions to address the multiple-rate requirement in applications. In the multi-objective evolutionary optimization framework, the fitness of each quantization table for JPEG compression is evaluated efficiently by the searching in a look-up table, which is constructed based on the statistics of each DCT band in a pre-defined manner. Then, the population update strategy based on the rate-distortion optimal principle is recommended to guide the evolution toward the best rate-distortion performance. Furthermore, to maintain the population's diversity and uniformity, convex-hull based environmental selection is recommended to identify the solutions at the first Pareto front, and the identified solutions are enriched further with scaling operation to fill the blank rate range. The experimental results for several classic datasets demonstrate the superiority of our method in terms of solution distribution, coding efficiency, and computational complexity. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a versatile and efficient quantization optimization method for image compression, based on multi-objective optimization and compatible with the JPEG standard. The method fully considers the rate-distortion optimal principle and provides multiple optimal solutions for applications with different rate requirements. Experimental results demonstrate the superiority of the proposed method in terms of solution distribution, coding efficiency, and computational complexity.