A SVD based scheme for post processing of DCT coded images

In block discrete cosine transform (DCT) based image compression the blocking artifacts are the main cause of degradation, especially at higher compression ratio. In proposed scheme, monotone or edge blocks are identified by examining the DCT coefficients of the block itself. In the first algorithm of the proposed scheme, a signal adaptive filter is applied to sub-image constructed by the DC components of DCT coded image to exploit the residual inter-block correlation between adjacent blocks. To further reduce artificial discontinuities due to blocking artifacts, the blocky image is re-divided into blocks in such a way that the corner of the original blocks comes at the center of new blocks. These discontinuities cause the high frequency components in the new blocks. In this paper, these high frequency components due to blocking artifacts in monotone area are eliminated using singular value decomposition (SVD) based filtering algorithm. It is well known that random noise is hard to compress whereas it is easy to compress the ordered information. Thus, lossy compression of noisy signal provides the required filtering of the signal

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

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

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

Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artifact removal and resolution enhancement. Based on maximum a posterior inference for estimating a clean low-resolution (LR) input image and a clean high resolution (HR) output image from down-sampled and compressed observations, we have designed a CISR architecture consisting of two deep neural network modules: the artifact reduction module (ARM) and resolution enhancement module (REM). ARM and REM work in parallel with both taking the compressed LR image as their inputs, while they also work in series with REM taking the output of ARM as one of its inputs and ARM taking the output of REM as its other input. A unique property of our CSIR system is that a single trained model is able to super-resolve LR images compressed by different methods to various qualities. This is achieved by exploiting deep neural net-works capacity for handling image degradations, and the parallel and series connections between ARM and REM to reduce the dependency on specific degradations. ARM and REM are trained simultaneously by the deep unfolding technique. Experiments are conducted on a mixture of JPEG and WebP compressed images without a priori knowledge of the compression type and com-pression factor. Visual and quantitative comparisons demonstrate the superiority of our method over state-of-the-art super resolu-tion methods.Code link: https://github.com/luohongming/CISR_PS

Video artefacts in mobile imaging devices

Master'sMASTER OF ENGINEERIN

Maximum Energy Subsampling: A General Scheme For Multi-resolution Image Representation And Analysis

Image descriptors play an important role in image representation and analysis. Multi-resolution image descriptors can effectively characterize complex images and extract their hidden information. Wavelets descriptors have been widely used in multi-resolution image analysis. However, making the wavelets transform shift and rotation invariant produces redundancy and requires complex matching processes. As to other multi-resolution descriptors, they usually depend on other theories or information, such as filtering function, prior-domain knowledge, etc.; that not only increases the computation complexity, but also generates errors. We propose a novel multi-resolution scheme that is capable of transforming any kind of image descriptor into its multi-resolution structure with high computation accuracy and efficiency. Our multi-resolution scheme is based on sub-sampling an image into an odd-even image tree. Through applying image descriptors to the odd-even image tree, we get the relative multi-resolution image descriptors. Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction. Since the maximum energy usually retained in the lowest frequency coefficients; we do maximum energy extraction through keeping the lowest coefficients from each resolution level. Our multi-resolution scheme can analyze images recursively and effectively without introducing artifacts or changes to the original images, produce multi-resolution representations, obtain higher resolution images only using information from lower resolutions, compress data, filter noise, extract effective image features and be implemented in parallel processing

Incrustation d'un logo dans un ficher vidéo codé avec le standard MPEG-2

Ce mémoire constitue l'aboutissement du projet de recherche de Patrick Keroulas et aborde la notion de compression vidéo, domaine en pleine ébullition avec la démocratisation de l'équipement vidéo et des réseaux de télécommunication. La question initiale est de savoir s'il est possible de modifier le contenu de l'image directement dans un flux binaire provenant d'une séquence vidéo compressée. Un tel dispositif permettrait d'ajouter des modifications en n'importe quel point d'un réseau en évitant le décodage et recodage du flux de données, ces deux processus étant très coûteux en termes de calcul. Brièvement présentés dans la première partie, plusieurs travaux ont déjà proposé une gamme assez large de méthodes de filtrage, de débruitage, de redimensionnement de l'image, etc. Toutes les publications rencontrées à ce sujet se concentrent sur la transposition des traitements de l'image du domaine spatial vers le domaine fréquentiel. Il a été convenu de centrer la problématique sur une application potentiellement exploitable dans le domaine de la télédiffusion. Il s'agit d'incruster un logo ajustable en position et en opacité dans un fichier vidéo codé avec la norme MPEG-2, encore couramment utilisée. La transformée appliquée par cet algorithme de compression est la DCT (Discrete Cosine Transform). Un article publié en 1995 traitant de la composition vidéo en général est plus détaillé car il sert de base à cette étude. Certains outils proposés qui reposent sur la linéarité et l'orthogonalité de la transformée seront repris dans le cadre de ce projet, mais la démarche proposée pour résoudre les problèmes temporels est différente. Ensuite, les éléments essentiels de la norme MPEG-2 sont présentés pour en comprendre les mécanismes et également pour exposer la structure d'un fichier codé car, en pratique, ce serait la seule donnée accessible. Le quatrième chapitre de l'étude présente la solution technique mise en oeuvre via un article soumis à IEEE Transactions on Broadcasting. C'est dans cette partie que toutes les subtilités liées au codage sont traitées : la structure en blocs de pixel, la prédiction spatiale, la compensation de mouvement au demi-pixel près, la nécessité ou non de la quantification inverse. À la vue des résultats satisfaisants, la discussion finale porte sur la limite du système : le compromis entre son efficacité, ses degrés de liberté et le degré de décodage du flux