A deblocking technique for block-transform compressed image using wavelet transform modulus maxima

In this work, we introduce a deblocking algorithm for Joint Photographic Experts Group (JPEG) decoded images using the wavelet transform modulus maxima (WTMM) representation. Under the WTMM representation, we can characterize the blocking effect of a JPEG decoded image as: 1) small modulus maxims at block boundaries over smooth regions: 2) noise or irregular structures near strong edges: and 3) corrupted edges across block boundaries, nle WTMM representation not only provides characterization of the blocking effect, but also enables simple and local operations to reduce the adverse effect due to this problem, The proposed algorithm first performs a segmentation on a JPEG decoded image to identify the texture regions by noting that their WTMM have small variation in regularity. We do not process the modulus maxima of these regions, to avoid the image texture being "oversmoothed" by the algorithm. There, the singularities in the remaining regions of the blocky image and thr small modulus maxima at block boundaries are removed. We link up the corrupted edges, and regularize the phase of modulus maxima as well as the magnitude of strong edges. Finally, the image is reconstructed using the projection onto convex set (POCS) technique [2] on the processed WTMM of that JPEG decoded image. This simple algorithm improves the quality of a JPEG decoded image in the senses of signal-to-noise ratio (SNR) as well as visual quality. We also compare the performance of our algorithm to the previous approaches, such as CLS and POCS methods. The most remarkable advantage of the WTMM deblocking algorithm is that we can directly process thf edges and texture of an image using its WTMM representation.