Multiscale probabilistic dithering for suppressing contour artifacts in digital images
IEEE Transactions on Image Processing
Block effect reduction by the 1-D gray polynomial interpolation
Digital Signal Processing
Learning-based image restoration for compressed images
Image Communication
Simple detection method and compensation filter to remove corner outlier artifacts
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Simple and effective filter to remove corner outlier artifacts in highly compressed video
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Detection method and removing filter for corner outliers in highly compressed video
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
| Hi-index | 0.00 |
The reconstructed images from highly compressed MPEG data have noticeable image degradations, such as blocking artifacts near the block boundaries, corner outliers at crosspoints of blocks, and ringing noise near image edges because the MPEG quantizes the transformed coefficients of 8×8 pixel blocks. A postprocessing algorithm is proposed to reduce quantization effects, such as blocking artifacts, corner outliers, and ringing noise, in MPEG-decompressed images. The proposed postprocessing algorithm reduces the quantization effects adaptively by using both spatial frequency and temporal information extracted from the compressed data. The blocking artifacts are reduced by one-dimensional (1-D) horizontal and vertical low-pass filtering (LPF), and the ringing noise is reduced by two-dimensional (2-D) signal-adaptive filtering (SAF). A comparison study of the peak signal-to-noise ratio (PSNR) and the computation complexity analysis between the proposed algorithm and the MPEG-4 VM (verification model) postprocessing algorithm is performed by computer simulation with several image sequences. According to the comparison study of PSNR and computation complexity analysis, the proposed algorithm shows better performance than the VM postprocessing algorithm, whereas the subjective image qualities of both algorithms are similar