Reduction of artifacts in cosine transform coded images
VIP '01 Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11
Reduction of blocking artifacts in JPEG compressed images
Digital Signal Processing
Temporal resolution enhancement in compressed video sequences
EURASIP Journal on Applied Signal Processing
Joint 3-D dynamic resolution conversion with rate-distortion for video coding
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Joint 3-D dynamic resolution conversion with rate-distortion for video coding
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
A document image model and estimation algorithm for optimized JPEG decompression
IEEE Transactions on Image Processing
Image postprocessing by Non-local Kuan's filter
Journal of Visual Communication and Image Representation
Image deblocking via sparse representation
Image Communication
Implementation and Optimization of an Enhanced PWD Metric for H.264/AVC on a TMS320C64 DSP
Journal of Signal Processing Systems
Non-causal temporal prior for video deblocking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Adaptive non-local means filter for image deblocking
Image Communication
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The discrete cosine transform (DCT) is the most popular transform for image and video compression. Many international standards such as JPEG, MPEG, and H.261 are based on a block-DCT scheme. High compression ratios are obtained by discarding information about DCT coefficients that is considered to be less important. The major drawback is visible discontinuities along block boundaries, commonly referred to as blocking artifacts. These often limit the maximum compression ratios that can be achieved. Various postprocessing techniques have been published that reduce these blocking effects, but most of them introduce unnecessary blurring, ringing, or other artifacts. In this paper, a novel postprocessing algorithm based on Markov random fields (MRFs) is proposed. It efficiently removes blocking effects while retaining the sharpness of the image and without introducing new artifacts. The degraded image is first segmented into regions, and then each region is enhanced separately to prevent blurring of dominant edges. A novel texture detector allows the segmentation of images containing both texture and monotone areas. It finds all texture regions in the image before the remaining monotone areas are segmented by an MRF segmentation algorithm that has a new edge component incorporated to detect dominant edges more reliably. The proposed enhancement stage then finds the maximum a posteriori estimate of the unknown original image, which is modeled by an MRF and is therefore Gibbs distributed. A very efficient implementation is presented. Experiments demonstrate that our proposed postprocessor gives excellent results compared to other approaches, from both a subjective and an objective viewpoint. Furthermore, it will be shown that our technique also works for wavelet encoded images, which typically contain ringing artifacts