Research On Quantization Table Optimization Of JPEG Image Coding Based On Evolutionary Computation

Due to the rapid development of science and technology,electronic devices such as smart phones and digital cameras have been widely used in people’s daily life,resulting in a substantial increase in the amount of image data,and this massive amount of image data poses a huge challenge to data storage and transmission.Under this background,it is required that the image compression technology can further reduce the data amount while image quality can be retained.Therefore,it is of great significance and value to optimize the image compression technology.As the most common compression standard for static images,JPEG has been used extensively across applications and devices to compress images,and it is still dominant in the field of image applications.For the JPEG encoder,optimizing the encoding efficiency under the premise of satisfying its compatibility has a wide range of application prospects.In the uncertain environment,the bit rate requirement is not clear,and it is necessary to generate multiple compressed images with different bit rates from the same original image to meet the requirements of various bit rates.However,most of the JPEG quantization table optimization algorithms do not consider this problem,and they are executed several times to adapt to a variety of code rates,and hence the optimization efficiency is very low.From this point of view,the integration of multi-objective optimization with JPEG quantization table optimization can achieve several optimal quantization tables in a optimization run,and these optimal quantization tables correspond to different bit rates which can effectively cope with the ambigous rate requirements in the uncertain environment.Therefore,in this thesis,the research of multiobjective evolutionary algorithm framework is conducted to obtain the optimal quantization tables of JPEG image compression.The main research work of this paper is as follows:(1)In this thesis,rate–distortion optimal evolutionary algorithm(RDOEA)for JPEG quantization with multiple rates is proposed.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 multiplerate 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.(2)In this thesis,the quantization table optimization algorithm for JPEG image coding based on bit rate constraints and visual subjective quality is proposed.In the RDOEA method,the optimization target is Mean Square Error,which is not consistent with the subjective quality of the image,and the JPEG standard adjusts different compression rates through quality factors,but the bit rate distribution obtained by different quality factors is uneven.The evolutionary optimization algorithm of JPEG encoding based on bit rate constraints and visual quality adopts a dual-population optimization framework,assists complex problems with simple problems,and improves the convergence speed of complex problems.In the optimization process,DCT Subbands Similarity is used as an image visual quality metric to balance constraints and target fitness values to improve JPEG encoding efficiency.The experimental results verify the effectiveness of the algorithm on the Kodak dataset.