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

Abstract

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.Index Terms-Adaptive quantization constraint set, blocking artifact, convergence rate, postprocessing, POCS.

Unified deblocking for discrete cosine transfer compressed images

“…Fifth row: Park and Lee, 16 25.89 dB; Choi and Kim, 59 25.96 dB; Wu et al,58 26.03 dB; Chen et al,56 26.14 dB. Sixth row: Al-Fohoum and Reza,7 26.05 dB; MPEG Video Group,47 24.67 dB; Ying and Ward,53 25.66 dB; Liew and Hong,18 26.02 dB.Table 1 Comparison of deblocking algorithms.…”

“…21 Observing the fact, Yang and Galatsanos 12 and Yang et al 22,23 proposed the block boundary smooth constraint set, adaptive smooth constraint set, and directional adaptive smooth constraint set to better describe the local smoothness of the image. Some variation of the smooth constraint set [24][25][26] and the local smooth constraint set 27 have also been proposed. At the same time, the quantization constraint set has been further developed into an adaptive quantization constraint set, 25 narrow quantization constraint set, 28 and local quantization set 29 for improved performance and fast convergence.…”

“…Some variation of the smooth constraint set [24][25][26] and the local smooth constraint set 27 have also been proposed. At the same time, the quantization constraint set has been further developed into an adaptive quantization constraint set, 25 narrow quantization constraint set, 28 and local quantization set 29 for improved performance and fast convergence. Stevenson was the first researcher to adapt the MAP estimation into the deblocking algorithm.…”