Information Theory and Coding for Image and Video Processing in the Era of Artificial Intelligence

Dear Colleagues,

Recent advances in artificial intelligence and deep learning are transforming the field of image and video processing. While classical information theory has long provided the theoretical foundation for image and video compression, emerging data-driven approaches are reshaping the design of coding algorithms and visual representation methods.

This Special Issue aims to present recent research developments at the intersection of information theory, signal processing, and artificial intelligence for image and video processing. Topics of interest include, but are not limited to, image and video compression, learned compression frameworks, entropy modeling, rate-distortion optimization, perceptual video coding, compressed sensing, and multimedia communication systems.

The Special Issue also welcomes contributions exploring AI-driven visual representation, machine learning for multimedia coding, visual data analysis in autonomous systems, and information-theoretic approaches to modern multimedia applications such as remote sensing, intelligent transportation, and edge multimedia systems.

Both theoretical contributions and application-oriented studies are encouraged. The goal of this Special Issue is to provide a platform for researchers working on next-generation multimedia processing systems and intelligent visual communication technologies.

Prof. Dr. Ofer Hadar
Guest Editor

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