Wavelet inpainting driven image compression via collaborative sparsity at low bit rates

To overcome the unsatisfactory encoding quality of conventional image compression methods at low bit rates, the idea of downsampling prior to encoding and upsampling after decoding turns out to be a good solution. Based on this paradigm, we propose a low-bit-rate image compression algorithm by use of the novel wavelet inpainting technique via collaborative sparsity. Superior to the existing methods which operate the sampling in the space domain, we merge the wavelet transform in the downsampling stage, which is verified to be able to preserve much more information. By investigating the local two-dimensional sparsity and the nonlocal three-dimensional sparsity of the image simultaneously, a collaborative sparsity model is exploited to restore the full-resolution image from the decoded downsampled image. Finally a Split Bregman based iterative algorithm is developed to solve the optimization problem. Experimental results demonstrate obvious visual quality improvements, as well as PSNR gains, compared to the state-of-the-art methods under various low bit rates.