A Genetic-Based Adaptive Threshold Selection Method for Dynamic Path Tree Structured Vector Quantization

[[abstract]]This paper presents an improvement method for enhancing the encoding time complexity of the dynamic path tree structured vector quantization (DPTSVQ) based on the same image quality. We call it the genetic-based adaptive threshold selection method (GATSM). DPTSVQ has successfully solved the disadvantage of the multi-path TSVQ. DPTSVQ uses a critical function and a fixed threshold to judge whether the number of search paths can be increased. However, in some cases, the fixed threshold scheme also brings the problem of increasing the encoding time. We thus propose GATSM to solve this problem by using a set of images to train the thresholds for adapting their real practical need. Our experimental results show that the encoding time complexity of GATSM is superior to DPTSVQ based on the same image quality. In addition, we compare the image quality of GATSM with the encoding algorithm with fast comparison (EAWFC) based on the same encoding time. Comparison results show that GATSM provides better image quality than that of EAWFC