JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures
This work addresses the challenge of balancing computational cost and image-specific optimality in JPEG compression, offering a practical solution for applications requiring efficient image encoding.
The paper tackles the problem of finding optimal JPEG quantization tables efficiently by introducing texture mosaic images and using simulated annealing to optimize tables per texture category, then fusing them based on predicted texture distributions, achieving a 23.5% size reduction over standard JPEG with minimal quality loss.
JPEG has been a widely used lossy image compression codec for nearly three decades. The JPEG standard allows to use customized quantization table; however, it's still a challenging problem to find an optimal quantization table within acceptable computational cost. This work tries to solve the dilemma of balancing between computational cost and image specific optimality by introducing a new concept of texture mosaic images. Instead of optimizing a single image or a collection of representative images, the simulated annealing technique is applied to texture mosaic images to search for an optimal quantization table for each texture category. We use pre-trained VGG-16 CNN model to learn those texture features and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image specific optimal quantization table. On the Kodak dataset with the quality setting $Q=95$, our experiment shows a size reduction of 23.5% over the JPEG standard table with a slightly 0.35% FSIM decrease, which is visually unperceivable. The proposed JQF method achieves per image optimality for JPEG encoding with less than one second additional timing cost. The online demo is available at https://matthorn.s3.amazonaws.com/JQF/qtbl_vis.html