JPEG Quantization Table Optimization Via Multi-objective Evolutionary Algorithm Based on Decomposition

The JPEG standard is still widely-used in current applications. The optimization on the quantization table in JPEG standard not only can maintain the compatibility with the existing JPEG standard, and but also can improve the coding efficiency of JPEG compression. In this paper, JPEG quantization table optimization via multi-objective evolutionary algorithm based on decomposition is proposed. 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 MOEA/D algorithm is utilized to minimize the rate and distortion simultaneously. After the multi-objective optimization, a set of non-dominated solutions (quantization tables) can be obtained. Therefore, our method can provide several optimal solutions to address the multiple-rate requirement in applications in one running time. The experimental results on the classic Kodak24 dataset demonstrate the superiority of our method in terms of coding efficiency and subjective quality.