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

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

Bilateral filter in image processing

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. It also can be applied to the blocking artifacts reduction. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. Another research interest of bilateral filter is acceleration of the computation speed. There are three main contributions of this thesis. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising. I propose an extension of the bilateral filter: multi resolution bilateral filter, where bilateral filtering is applied to the low-frequency sub-bands of a signal decomposed using a wavelet filter bank. The multi resolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. The second contribution is that I present a spatially adaptive method to reduce compression artifacts. To avoid over-smoothing texture regions and to effectively eliminate blocking and ringing artifacts, in this paper, texture regions and block boundary discontinuities are first detected; these are then used to control/adapt the spatial and intensity parameters of the bilateral filter. The test results prove that the adaptive method can improve the quality of restored images significantly better than the standard bilateral filter. The third contribution is the improvement of the fast bilateral filter, in which I use a combination of multi windows to approximate the Gaussian filter more precisely

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

Removal Of Blocking Artifacts From JPEG-Compressed Images Using Neural Network

The goal of this research was to develop a neural network that will produce considerable improvement in the quality of JPEG compressed images, irrespective of compression level present in the images. In order to develop a computationally efficient algorithm for reducing blocky and Gibbs oscillation artifacts from JPEG compressed images, we integrated artificial intelligence to remove blocky and Gibbs oscillation artifacts. In this approach, alpha blend filter [7] was used to post process JPEG compressed images to reduce noise and artifacts without losing image details. Here alpha blending was controlled by a limit factor that considers the amount of compression present, and any local information derived from Prewitt filter application in the input JPEG image. The outcome of modified alpha blend was improved by a trained neural network and compared with various other published works [7][9][11][14][20][23][30][32][33][35][37] where authors used post compression filtering methods

Microarray missing data imputation based on a set theoretic framework and biological knowledge

Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods

Continuum Modeling of Cell Contractility

Biological cells generate mechanical forces to sense and interact with neighboring cells and the extracellular environment. In this thesis, I combine traction force microscopy, viscoelastic continuum models, finite element simulations and homogenization techniques to demonstrate how contractility on the cellular level emerges from the force-generating actomyosin cytoskeleton. These theoretical approaches are complemented by a series of collaborations with experimental groups that investigate the actomyosin system in different biological systems. For stress fibers, we find a transition from elastic to fluid behavior at a typical timescale of tens of minutes. For small yet strong spreading platelets, we estimate intracellular stresses in the kilopascal range. For epithelial monolayers, I show that the propagation of mechanical forces defines the territories for leader cell formation. Homogenization is used to demonstrate how intracellular polarization determines traction forces, and that stress fibers are characterized by negative compressibility, a property which defines mechanical metamaterials