Knowledge-based differential evolution approach to quantisation table generation for the JPEG baseline algorithm

摘要

Image quality/compression trade-off mainly depends on quantisation table used in JPEG scheme. Therefore, the generation of quantisation table is an optimisation problem. Even though recent reports reveal that classical differential evolution (CDE) is a promising algorithm to generate the optimal quantisation table, it is slow in convergence rate due to its weak local exploitation ability. This paper proposes knowledge-based differential evolution (KBDE) algorithm to search the optimal quantisation table for the target bits/pixel (bpp). KBDE incorporates the image characteristics and knowledge about image compressibility in CDE operators to accelerate the search. KBDE and CDE algorithms have been experimented on variety of images and an extensive performance analysis has been made between them, which reveal that KBDE accelerates the convergence rate of CDE without compromising on the quality of solution. Further, a statistical hypothesis test (t-test) confirms the result.