Machine-Learning Based Secondary Transform For Improved Image Compression In Jpeg2000

This paper proposes a convolutional neural network (CNN) based secondary transform for not only improved coding efficiency in the JPEG2000 image compression format, but also to produce more appealing approximation sub-bands at different resolutions. The CNN in this work exploits information in detail sub-bands to predict some of the aliasing information in the corresponding approximation or low-pass sub-band; this reduction in aliasing, although not perfect, improves the compressibility of the "cleaned'' approximation sub-band. This process is repeated in subsequent wavelet decomposition levels to further improve coding efficiency. Experimental results show that, at high bit rates, the proposed network outperforms conventional JPEG2000 compression framework by up to 1.2 dB, especially for images with strong geometric flow.