A Neural Network Lifting Based Secondary Transform for Improved Fully Scalable Image Compression in Jpeg 2000

This paper proposes a secondary transform to improve wavelet-based image compression schemes such as JPEG 2000. The proposed approach includes two neural network steps, a high-to-low step followed by a low-to-high step. The high-to-low step suppresses aliasing in the low-pass band by using the detail bands at the same resolution, while the low-to-high step aims to further remove redundant information from the detail bands so as to achieve higher energy compaction. We employ the same neural networks for these two steps at every level of the hierarchical discrete wavelet transform (DWT) decomposition. We demonstrate that the visual quality of the LL bands at different resolutions can be dramatically enhanced, along with greatly improved coding efficiency up to 2dB compared with JPEG 2000, while preserving the full quality and resolution scalability and spatial random access features of JPEG 2000.