Compression Artifact Reduction using Support Vector Regression
In this paper, we propose a compression artifact reduction algorithm based on ν support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to artifact reduction methods specific to each type of compression artifact (e.g. blocking, ringing etc), we treat such different artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and artifact-corrupted image) learned during the training step. Experimental results exhibit significant reduction in all types of compression artifacts.