On the Application of Dictionary Learning to Image Compression

Signal models are a cornerstone of contemporary signal and image-processing methodology. In this chapter, a particular signal modelling method, called synthesis sparse representation, is studied which has been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. In this kind of signal modelling, the signal is represented with respect to dictionary. The dictionary choice plays an important role on the success of the entire model. One main discipline of dictionary designing is based on a machine learning methodology which provides a simple and expressive structure for designing adaptable and efficient dictionaries. This chapter focuses on direct application of the sparse representation, i.e. image compression. Two image codec based on adaptive sparse representation over a trained dictionary are introduced. Experimental results show that the presented methods outperform the existing image coding standards, such as JPEG and JPEG2000

CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution

Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance. In order to further improve the reconstruction capability of current methods of compressed image super-resolution, a circular Swin2SR (CSwin2SR) approach is proposed. The CSwin2SR contains a serial Swin2SR for initial super-resolution reestablishment and circular Swin2SR for enhanced super-resolution reestablishment. Simulated experimental results show that the proposed CSwin2SR dramatically outperforms the classical Swin2SR in the capacity of super-resolution recovery. On DIV2K test and valid datasets, the average increment of PSNR is greater than 1dB and the related average increment of SSIM is greater than 0.006

Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction

In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N000141410355European Union H2020 76586

CMISR: Circular Medical Image Super-Resolution

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic which can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast

Cascade Decoders-Based Autoencoders for Image Reconstruction

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction

Disparity-Compensated Compressed-Sensing Reconstruction for Multiview Images

In a multiview-imaging setting, image-acquisition costs could be substantially diminished if some of the cameras operate at a reduced quality. Compressed sensing is proposed to effectuate such a reduction in image quality wherein certain images are acquired with random measurements at a reduced sampling rate via projection onto a random basis of lower dimension. To recover such projected images, compressed-sensing recovery incorporating disparity compensation is employed. Based on a recent compressed-sensing recovery algorithm for images that couples an iterative projection-based reconstruction with a smoothing step, the proposed algorithm drives image recovery using the projection-domain residual between the random measurements of the image in question and a disparity-based prediction created from adjacent, high-quality images. Experimental results reveal that the disparity-based reconstruction significantly outperforms direct reconstruction using simply the random measurements of the image alone

Décompositions spatio-temporelles et allocation de débit utilisant les coupures des graphes pour le codage vidéo scalable

The recent progress in wavelet-based video coding schemes led to the emergence of a new generation of scalable video codecs, whose performance is comparable to the best hybrid codecs. The t+2D wavelet-based schemes exploit the temporal interframe redundancy by applying an open-loop temporal wavelet transform over the frames of a video sequence. Temporally filtered subband frames are further spatially decomposed and can be encoded by different entropy-coding algorithms. Because of their inherent multiresolution signal representation, wavelet-based coding schemes have the potential to support temporal, spatial and SNR scalability. This is the reason for which we have chosen the scalable lifting-based wavelet-coding paradigm as the conceptual development framework for this thesis. The objective of this work consists of the analysis and design of a scalable video coding system. In a first time, we are interested in the construction and optimization of new motion-compensated temporal coding schemes, in order to increase the efficiency of objective and subjective video coding. Moreover, we describe a better representation of temporal subbands, by using anisotropic spatial decompositions, for a better capture of the orientation of spatial details. Finally, we propose a method for improving the entropy coding by designing a graph-based solution, in order to optimize the minimization of the Lagrangian rate-distortion functional.Les progrés récents dans le domaine des schémas de codage vidéo par ondelettes ont permis l'apparition d'une nouvelle génération de codeurs vidéo scalables dont l'efficacité est comparable à celle des meilleurs codecs hybrides. Ces schémas sont qualifiés de t+2D et reposent sur l'utilisation d'une transformée en ondelettes appliquée le long du mouvement des images afin d'exploiter leur redondance temporelle. Les sous-bandes résultantes sont alors décomposées spatialement et encodées par un codeur entropique. Grâce à la représentation multirésolution inhérente, les codeurs basés-ondelettes ont la capacité de fournir une description scalable d'un signal. Ceci représente la raison principale pour laquelle le choix du paradigme du codage lifting t+2D basé-ondelettes s'impose comme cadre conceptuel de développement pour les travaux dans cette thèse. L'objectif de ces travaux consiste en l'analyse et la conception d'un système de codage vidéo scalable. Dans un premier temps, nous nous intéressons à la construction et l'optimi\-sation de nouvelles transformées temporelles compensées en mouvement, dans le but d'augmenter l'efficacité objective et subjective du codage. En outre, nous décrivons une meilleure représentation des sous-bandes temporelles, en utilisant des décompositions spatiales anisotropes afin de capturer l'orientation spatiale de détails. Enfin, nous proposons une methode d'amélioration du codage entropique en concevant une solution basée sur la théorie des graphes afin d'optimiser la minimisation du Lagrangien débit-distorsion