Postprocessing of images coded using block DCT at low bit rates.

Sun, Deqing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 86-91).Abstracts in English and Chinese.Abstract --- p.i摘要 --- p.iiiContributions --- p.ivAcknowledgement --- p.viAbbreviations --- p.xviiiNotations --- p.xxiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Image compression and postprocessing --- p.1Chapter 1.2 --- A brief review of postprocessing --- p.3Chapter 1.3 --- Objective and methodology of the research --- p.7Chapter 1.4 --- Thesis organization --- p.8Chapter 1.5 --- A note on publication --- p.9Chapter 2 --- Background Study --- p.11Chapter 2.1 --- Image models --- p.11Chapter 2.1.1 --- Minimum edge difference (MED) criterion for block boundaries --- p.12Chapter 2.1.2 --- van Beek's edge model for an edge --- p.15Chapter 2.1.3 --- Fields of experts (FoE) for an image --- p.16Chapter 2.2 --- Degradation models --- p.20Chapter 2.2.1 --- Quantization constraint set (QCS) and uniform noise --- p.21Chapter 2.2.2 --- Narrow quantization constraint set (NQCS) --- p.22Chapter 2.2.3 --- Gaussian noise --- p.22Chapter 2.2.4 --- Edge width enlargement after quantization --- p.25Chapter 2.3 --- Use of these models for postprocessing --- p.27Chapter 2.3.1 --- MED and edge models --- p.27Chapter 2.3.2 --- The FoE prior model --- p.27Chapter 3 --- Postprocessing using MED and edge models --- p.28Chapter 3.1 --- Blocking artifacts suppression by coefficient restoration --- p.29Chapter 3.1.1 --- AC coefficient restoration by MED --- p.29Chapter 3.1.2 --- General derivation --- p.31Chapter 3.2 --- Detailed algorithm --- p.34Chapter 3.2.1 --- Edge identification --- p.36Chapter 3.2.2 --- Region classification --- p.36Chapter 3.2.3 --- Edge reconstruction --- p.37Chapter 3.2.4 --- Image reconstruction --- p.37Chapter 3.3 --- Experimental results --- p.38Chapter 3.3.1 --- Results of the proposed method --- p.38Chapter 3.3.2 --- Comparison with one wavelet-based method --- p.39Chapter 3.4 --- On the global minimum of the edge difference . . --- p.41Chapter 3.4.1 --- The constrained minimization problem . . --- p.41Chapter 3.4.2 --- Experimental examination --- p.42Chapter 3.4.3 --- Discussions --- p.43Chapter 3.5 --- Conclusions --- p.44Chapter 4 --- Postprocessing by the MAP criterion using FoE --- p.49Chapter 4.1 --- The proposed method --- p.49Chapter 4.1.1 --- The MAP criterion --- p.49Chapter 4.1.2 --- The optimization problem --- p.51Chapter 4.2 --- Experimental results --- p.52Chapter 4.2.1 --- Setting algorithm parameters --- p.53Chapter 4.2.2 --- Results --- p.56Chapter 4.3 --- Investigation on the quantization noise model . . --- p.58Chapter 4.4 --- Conclusions --- p.61Chapter 5 --- Conclusion --- p.71Chapter 5.1 --- Contributions --- p.71Chapter 5.1.1 --- Extension of the DCCR algorithm --- p.71Chapter 5.1.2 --- Examination of the MED criterion --- p.72Chapter 5.1.3 --- Use of the FoE prior in postprocessing . . --- p.72Chapter 5.1.4 --- Investigation on the quantization noise model --- p.73Chapter 5.2 --- Future work --- p.73Chapter 5.2.1 --- Degradation model --- p.73Chapter 5.2.2 --- Efficient implementation of the MAP method --- p.74Chapter 5.2.3 --- Postprocessing of compressed video --- p.75Chapter A --- Detailed derivation of coefficient restoration --- p.76Chapter B --- Implementation details of the FoE prior --- p.81Chapter B.1 --- The FoE prior model --- p.81Chapter B.2 --- Energy function and its gradient --- p.83Chapter B.3 --- Conjugate gradient descent method --- p.84Bibliography --- p.8

Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution

Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images

On Using and Improving Gradient Domain Processing for Image Enhancement

Ph.DDOCTOR OF PHILOSOPH

A learning-by-example method for reducing BDCT compression artifacts in high-contrast images.

Wang, Guangyu.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-75).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- BDCT Compression Artifacts --- p.1Chapter 1.2 --- Previous Artifact Removal Methods --- p.3Chapter 1.3 --- Our Method --- p.4Chapter 1.4 --- Structure of the Thesis --- p.4Chapter 2 --- Related Work --- p.6Chapter 2.1 --- Image Compression --- p.6Chapter 2.2 --- A Typical BDCT Compression: Baseline JPEG --- p.7Chapter 2.3 --- Existing Artifact Removal Methods --- p.10Chapter 2.3.1 --- Post-Filtering --- p.10Chapter 2.3.2 --- Projection onto Convex Sets --- p.12Chapter 2.3.3 --- Learning by Examples --- p.13Chapter 2.4 --- Other Related Work --- p.14Chapter 3 --- Contamination as Markov Random Field --- p.17Chapter 3.1 --- Markov Random Field --- p.17Chapter 3.2 --- Contamination as MRF --- p.18Chapter 4 --- Training Set Preparation --- p.22Chapter 4.1 --- Training Images Selection --- p.22Chapter 4.2 --- Bit Rate --- p.23Chapter 5 --- Artifact Vectors --- p.26Chapter 5.1 --- Formation of Artifact Vectors --- p.26Chapter 5.2 --- Luminance Remapping --- p.29Chapter 5.3 --- Dominant Implication --- p.29Chapter 6 --- Tree-Structured Vector Quantization --- p.32Chapter 6.1 --- Background --- p.32Chapter 6.1.1 --- Vector Quantization --- p.32Chapter 6.1.2 --- Tree-Structured Vector Quantization --- p.33Chapter 6.1.3 --- K-Means Clustering --- p.34Chapter 6.2 --- TSVQ in Artifact Removal --- p.35Chapter 7 --- Synthesis --- p.39Chapter 7.1 --- Color Processing --- p.39Chapter 7.2 --- Artifact Removal --- p.40Chapter 7.3 --- Selective Rejection of Synthesized Values --- p.42Chapter 8 --- Experimental Results --- p.48Chapter 8.1 --- Image Quality Assessments --- p.48Chapter 8.1.1 --- Peak Signal-Noise Ratio --- p.48Chapter 8.1.2 --- Mean Structural SIMilarity --- p.49Chapter 8.2 --- Performance --- p.50Chapter 8.3 --- How Size of Training Set Affects the Performance --- p.52Chapter 8.4 --- How Bit Rates Affect the Performance --- p.54Chapter 8.5 --- Comparisons --- p.56Chapter 9 --- Conclusion --- p.61Chapter A --- Color Transformation --- p.63Chapter B --- Image Quality --- p.64Chapter B.1 --- Image Quality vs. Quantization Table --- p.64Chapter B.2 --- Image Quality vs. Bit Rate --- p.66Chapter C --- Arti User's Manual --- p.68Bibliography --- p.7

Mathematical Approaches for Image Enhancement Problems

This thesis develops novel techniques that can solve some image enhancement problems using theoretically and technically proven and very useful mathematical tools to image processing such as wavelet transforms, partial differential equations, and variational models. Three subtopics are mainly covered. First, color image denoising framework is introduced to achieve high quality denoising results by considering correlations between color components while existing denoising approaches can be plugged in flexibly. Second, a new and efficient framework for image contrast and color enhancement in the compressed wavelet domain is proposed. The proposed approach is capable of enhancing both global and local contrast and brightness as well as preserving color consistency. The framework does not require inverse transform for image enhancement since linear scale factors are directly applied to both scaling and wavelet coefficients in the compressed domain, which results in high computational efficiency. Also contaminated noise in the image can be efficiently reduced by introducing wavelet shrinkage terms adaptively in different scales. The proposed method is able to enhance a wavelet-coded image computationally efficiently with high image quality and less noise or other artifact. The experimental results show that the proposed method produces encouraging results both visually and numerically compared to some existing approaches. Finally, image inpainting problem is discussed. Literature review, psychological analysis, and challenges on image inpainting problem and related topics are described. An inpainting algorithm using energy minimization and texture mapping is proposed. Mumford-Shah energy minimization model detects and preserves edges in the inpainting domain by detecting both the main structure and the detailed edges. This approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. The estimated segmentation results in the inpainting domain are stored in segmentation map, which is referred by a texture mapping algorithm for filling textured regions. We also propose an inpainting algorithm using wavelet transform that can expect better global structure estimation of the unknown region in addition to shape and texture properties since wavelet transforms have been used for various image analysis problems due to its nice multi-resolution properties and decoupling characteristics

Visual Saliency Estimation Via HEVC Bitstream Analysis

Abstract Since Information Technology developed dramatically from the last century 50's, digital images and video are ubiquitous. In the last decade, image and video processing have become more and more popular in biomedical, industrial, art and other fields. People made progress in the visual information such as images or video display, storage and transmission. The attendant problem is that video processing tasks in time domain become particularly arduous. Based on the study of the existing compressed domain video saliency detection model, a new saliency estimation model for video based on High Efficiency Video Coding (HEVC) is presented. First, the relative features are extracted from HEVC encoded bitstream. The naive Bayesian model is used to train and test features based on original YUV videos and ground truth. The intra frame saliency map can be achieved after training and testing intra features. And inter frame saliency can be achieved by intra saliency with moving motion vectors. The ROC of our proposed intra mode is 0.9561. Other classification methods such as support vector machine (SVM), k nearest neighbors (KNN) and the decision tree are presented to compare the experimental outcomes. The variety of compression ratio has been analysis to affect the saliency