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The quantization table in the baseline Joint Photographic Experts Group (JPEG) algorithm plays an important role in compression/quality trade-off. Hence the detection of the optimal quantization table is viewed as an optimization problem. The genetic algorithm (GA) is an attractive optimization tool by many researchers for this application due to its ability in dealing with complex problems. In spite of its advantages, the GA requires more computation time to achieve an optimal solution if it has an expensive fitness evaluation. This paper proposes a problem approximation surrogate model (PASM) for fitness approximation to assist the GA in optimizing the quantization table for a target bits per pixel. This proposal reduces the computational time of the GA without any loss in performance. The PASM uses an image block clustering process and an indirect evaluation method to approximate the fitness value. The number of clusters in the clustering process may influence the performance of the PASM. A performance analysis with different number of clusters has been done and a suitable cluster number is identified with the help of measuring criteria such as mean squared difference, correct selection, potentially correct selection, and rank correlation. In addition, the results acquired from these measuring criteria are confirmed using statistical hypothesis tests such as Friedman’s ANOVA and Wilcoxon signed rank. The PASM with suitable cluster number has been tested in a classical genetic algorithm and knowledge based genetic algorithm. Several benchmark images with different complexity levels have been examined in three different target bits per pixel to validate the performance of the PASM. The results proved that the PASM guarantees better results in terms of peak signal-to-noise ratio with a reduction in computational time.

A problem approximation surrogate model (PASM) for fitness approximation in optimizing the quantization table for the JPEG baseline algorithm