On the POCS-based postprocessing technique to reduce the blocking artifacts in transform coded images

Abstract

In this paper, we propose a novel postprocessing technique, based on the theory of projections onto convex sets (POCS), to reduce the blocking artifacts in transform-coded images. It is assumed, in our approach, that the original image is highly correlated. Thus, the global frequency characteristics in two adjacent blocks are similar to the local ones in each block. We consider the high-frequency components in the global characteristics of a decoded image, which are not found in the local ones, as the results from the blocking artifact. We employ N-point discrete cosine transform (DCT) to obtain the local characteristics, and 2N -point DCT to obtain the global ones, and then derive the relation between N-point and 2N -point DCT coefficients. A careful comparison of N-point with 2N -point DCT coefficients makes it possible to detect the undesired highfrequency components, mainly caused by the blocking artifact. Then, we propose novel convex sets and their projection operators in the DCT domain. The performances of the proposed and conventional techniques are compared on the still images, decoded by JPEG. The results show that, regardless of the content of the input images, the proposed technique yields significantly better performance than the conventional techniques in terms of objective quality, subjective quality, and convergence behavior.

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.…”

Algorithm for JPEG artifact reduction via local edge regeneration

“…Accordingly, most artifact-reduction algorithms follow this latter approach. Postprocessing can roughly be divided into spatial-domain techniques, [13][14][15][16][17][18][19][20] DCT-domain techniques, [21][22][23][24][25][26] projections onto convex sets (POCS), [27][28][29][30][31] and block-shift filtering. [32][33][34][35][36][37][38][39][40][41] The spatial-domain techniques process the compressed image based on some prior knowledge and information about the original image, such as intensity smoothness or block boundaries of images, to improve the image quality.…”

Optimization method to reduce blocking artifacts in JPEG images

“…The output was considered to be the average of all the decompressed shifted images. In another POCS-based method, 12 N-point and 2N-point one-dimensional DCTs were used to represent the frequency characteristics of adjacent blocks. In the work in Ref.…”