Improved Transform Structures for Learned Wavelet-Like Fully Scalable Image Compression
Xinyue Li, Aous Thabit Naman, David Taubman · 2023
This paper studies features and performance of different structures for learned wavelet-like transforms in fully scalable image compression. Specifically, we explore different neural network topologies and various arrangements of lifting steps to improve the existing wavelet transform. Experimental results strongly suggest that the proposed proposal-opacity network topology, comprising a collection of linear predictions modulated by non-linear opacities, performs better than the other considered designs. Results also show that augmenting a good base wavelet transform with two learned lifting steps performs significantly better than other learned lifting structures, achieving 26.8% bit-rate savings over JPEG 2000.