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JPEG has been a widely used lossy image compression codec for nearly threedecades. The JPEG standard allows to use customized quantization table;however, it's still a challenging problem to find an optimal quantization tablewithin acceptable computational cost. This work tries to solve the dilemma ofbalancing between computational cost and image specific optimality byintroducing a new concept of texture mosaic images. Instead of optimizing asingle image or a collection of representative images, the simulated annealingtechnique is applied to texture mosaic images to search for an optimalquantization table for each texture category. We use pre-trained VGG-16 CNNmodel to learn those texture features and predict the new image's texturedistribution, then fuse optimal texture tables to come out with an imagespecific optimal quantization table. On the Kodak dataset with the qualitysetting $Q=95$, our experiment shows a size reduction of 23.5% over the JPEGstandard table with a slightly 0.35% FSIM decrease, which is visuallyunperceivable. The proposed JQF method achieves per image optimality for JPEGencoding with less than one second additional timing cost. The online demo isavailable at https://matthorn.s3.amazonaws.com/JQF/qtbl_vis.html

JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures