Low bit rates image coding via wavelet domain downsampling and dual wavelet pixel domain reconstruction
At low bit rates, the conventional image coding standards, e.g. JPEG and JPEG 2000, do not have good compression performance due to the insufficiency of coding bits. A common solution to this problem is downsampling before encoding and reconstruction after decoding. Inspired by the wavelet domain downsampling based compression scheme, we establish an enhanced low bit rates coding framework by making the following improvements. Firstly, a regression priors based coding artifacts reduction (RCAR) method is incorporated to preprocess the decoded low resolution (LR) image; secondly, given the decoded low frequency wavelet coefficients, we propose to estimate its corresponding high frequency wavelet coefficients by using the joint optimized regressors (JOR) model to recover more information lost in downsampling phase; finally, the effective group based sparse representation (GSR) model, which exploits both the nonlocal self similarity and local sparsity properties, is utilized to perform soft decoding on the result of wavelet reconstruction. Experimental results suggest that the proposed framework outperforms JPEG 2000 at low to medium bit rates in terms of both quantitative and visual comparisons.
2016 IEEE 13th International Conference on Signal Processing (ICSP)