Optimal JPEG Quantization Table Fusion by Optimizing Texture Mosaic Images and Predicting Textures

The JPEG standard allows the use of a customized quantization table; however, it is still challenging to find an optimal quantization table timely. This work aims to solve the dilemma of balancing 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 conventional JPEG optimization techniques can be applied to the texture mosaic image to obtain an optimal quantization table for each texture category. We use the simulated annealing technique as an example to validate our framework. To effectively learn the visual features of textures, we use the ImageNet pre-trained MobileNetV2 model to train and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image-specific optimal quantization table. Our experiment demonstrates around 30% size reduction with a slight decrease of FSIM quality but visually indistinguishable on the evaluation datasets. Moreover, our rate-distortion curve shows superior and competitive performance against other prior works under a high-quality setting. The proposed method, denoted as JQF, achieves per image optimality for JPEG encoding with less than one second additional timing cost.

Mathematical models for calculating the quantitative characteristics of the optimal quantization of information

Various additional mathematical aspects related to solving the problem of optimal information quantization in the sense of filling are considered, such as control of quantum elements, accounting for errors of quantum elements, determining the amount of information during quantization, and determining the numerical values of fractals of distributions represented as a sequential fractal distribution. The purpose of the article is to consider additional questions based on a specific "heavy" probability distribution the normal distribution. The considered questions are made in order to facilitate the solution of applied problems for researchers dealing with the problem of information quantization.

Quantization for a Mixture of Uniform Distributions Associated with Probability Vectors

The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus approximation of a continuous probability distribution by a discrete distribution. Mixtures of probability distributions, also known as mixed distributions, are an exciting new area for optimal quantization. In this paper, we investigate the optimal quantization for three different mixed distributions generated by uniform distributions associated with probability vectors.