We propose a convolutional neural network (CNN) based image compression scheme that is compatible with JPEG-2000. Specifically, our scheme reuses the existing JPEG-2000 encoders to achieve bitstream, and features two components in addition to JPEG-2000: bitstream re-compression and decoder-side post-processing. First, we propose an advanced arithmetic codec that adopts CNN-based probability estimation to exploit the correlation between wavelet coefficients within and across subbands. Second, we propose a CNN-based post-processing method to improve the quality of reconstructed images. Experimental results show that the proposed two CNN-based components both help improve the compression efficiency by a significant margin.
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL