Removal Of Blocking Artifacts From JPEG-Compressed Images Using An Adaptive Filtering Algorithm

The aim of this research was to develop an algorithm that will produce a considerable improvement in the quality of JPEG images, by removing blocking and ringing artifacts, irrespective of the level of compression present in the image. We review multiple published related works, and finally present a computationally efficient algorithm for reducing the blocky and Gibbs oscillation artifacts commonly present in JPEG compressed images. The algorithm alpha-blends a smoothed version of the image with the original image; however, the blending is controlled by a limit factor that considers the amount of compression present and any local edge information derived from the application of a Prewitt filter. In addition, the actual value of the blending coefficient (α) is derived from the local Mean Structural Similarity Index Measure (MSSIM) which is also adjusted by a factor that also considers the amount of compression present. We also present our results as well as the results for a variety of other papers whose authors used other post compression filtering methods

FAST ADAPTIVE FUZZY POST-FILTERING FOR CODING ARTIFACTS REMOVAL IN INTERLACED VIDEO

This paper presents a new method using fuzzy filtering to remove the coding artifacts in compressed video. The method takes interlaced video format into consideration and processes each field format into consideration and processes each field separately. For deblocking, a 1-D fuzzy filter with different window size is used to remove the horizontal and vertical blocking artifacts respectively. For deringing, each 8 X 8 block in a field is first classified into one of the four categories, i.e., strong edge, weak edge, texture and smooth blocks. According to each block´s type and the neighboring block´s type, the spread parameter of a 2-D fuzzy filter is adaptively decided and the filter is applied. To speed up the proces, the fuzzy filter weights are generated using a piecewise linear membership function instead of the conventional Gaussian function. The experimental results show that the proposed method has better detail preservation and lower computational costs than our previous method. It achieves comparable deblocking and superior deringing performance to the MPEG-4 standard method at similar computation costs. IEEE, ICASSP 0

Video Filtering Using Separable Four-Dimensional Nonlocal Spatiotemporal Transforms

The large number of practical application involving digital videos has motivated a significant interest in restoration or enhancement solutions to improve the visual quality under the presence of noise. We propose a powerful video denoising algorithm that exploits temporal and spatial redundancy characterizing natural video sequences to reduce the effects of noise. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a four-dimensional transform- domain representation is leveraged to enforce sparsity and thus regularize the data. Moreover we present an extension of our algorithm that can be effectively used as a deblocking and deringing filter to reduce the artifacts introduced by most of the popular video compression techniques. Our algorithm, termed V-BM4D, at first constructs three-dimensional volumes, by tracking blocks along trajectories defined by the motion vectors, and then groups together mutually similar volumes by stacking them along an additional fourth dimension. Each group is transformed through a decorrelating four-dimensional separable transform, and then it is collaboratively filtered by coeffcients shrinkage. The effectiveness of shrinkage is due to the sparse representation of the transformed group. Sparsity is achieved because of different type of correlation among the groups: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation along the fourth dimension. As a conclusive step, the different estimates of the filtered groups are adaptively aggregated and subsequently returned to their original position, to produce a final estimate of the original video. The proposed filtering procedure leads to excellent results in both objective and subjective visual quality, since in the restored video sequences the effect of the noise or of the compression artifacts is noticeably reduced, while the significant features are preserved. As demonstrated by experimental results, V-BM4D outperforms the state of the art in video denoising. /Kir1