Abstract:
A fuzzy filter adaptive to both sample’s activity and the relative position between samples is proposed to reduce the artifacts in compressed multidimensional signals. For JPEG images, the fuzzy spatial filter is based on the directional characteristics of ringing artifacts along the strong edges. For compressed video sequences, the motion compensated spatiotemporal filter (MCSTF) is applied to intra frame and inter frame pixels to deal with both spatial and temporal artifacts. A new metric which considers the tracking characteristic of human eyes is proposed to evaluate the flickering artifacts. Simulations on compressed images and videos show improvement in artifact reduction of the proposed adaptive fuzzy filter over other conventional spatial or temporal filtering approaches.
INTRODUCTION:
Block based compressed signals suffer from blocking, ringing, mosquito, and flickering artifacts, especially at low-bit-rate coding. Separately compressing each block breaks the correlation between pixels at the border of neighboring blocks and causes blocking artifacts. Ringing artifacts occur due to the loss of high frequencies when quantizing the DCT coefficients with a coarse quantization step. Ringing artifacts are similar to the Gibbs phenomenon and are most prevalent along the strong edges. On the order hand, mosquito artifacts come from ringing artifacts of many single compressed frames when displayed in a sequence. For inter coded frames, mosquito artifacts become more annoying for blocks on the boundary of moving object and background which have significant inter frame prediction errors in the residual signal Flickering artifacts happen due to the inconsistency in quality over frames at the same spatial position. This inconsistency is from the temporal distortion over compressed frames caused by quantizing the residual signal. These flickering artifacts, which are perceived more in the flat areas, also come from different quantization levels for rate-distortion optimization.
Many filter-based de noising methods have been proposed to reduce these artifacts, most of which are frame-based enhancement. For blocking artifact reduction, a linear low-pass filter was used into remove the high frequencies caused by blocky edges at borders, but excessive blur was introduced since the high frequencies components of the image were also removed. In low-pass filters were applied to the DCT coefficients of shifted blocks. In particular, the adaptive linear filters in and were proposed to overcome the problem of over-blurring the images, but these methods require high computational complexity. In a project onto convex set-based method was proposed with multi frame constraint sets to reduce the blocking artifacts. This method required to extract the motion between frames and quantization information from the video bit-stream.
To reduce ringing artifacts, the methods and utilized the linear or nonlinear isotropic filters to the ringing areas. As an encoder-based approach, proposed a noise shaping algorithm to find the optimal DCT coefficients which adapts to the noise variances in different areas. All of these methods can only reduce ringing artifacts in each frame. To deal with the temporal characteristic of mosquito artifacts, applied the spatiotemporal median filter in transform domain for surrounding 8×8 blocks. The improvement in this case is limited by the small correlation between DCT coefficients of the spatial neighboring 8×8 blocks as well as the lack of motion compensation in the scheme.