A practical projection-based postprocessing of block-coded images with fast convergence rate

At present, block-transform coding is probably the most popular approach for image compression. However, for the sake of its implementation, an image is partitioned into spatially adjoining blocks which are processed independently without considering inter-block correlation. So, this approach inescapably causes an annoying defect called a blocking artifact. In this letter, in order to reduce a blocking artifact appearing in block-coded images, a new quantization constraint set based on the theory of projection onto convex set (POCS) is proposed. This set can efficiently complement the drawbacks of the projection onto the other constraint sets, particularly the smoothness constraint set. Experimental results, using the proposed quantization constraint set as a substitute for the conventional quantization constraint set, show that the postprocessed images not only converge at a fast rate but also obtain better performance in both objective and subjective quality. Moreover, we know that the postprocessed images maintain the clearness of the decoded image before postprocessing