000170277 001__ 170277
000170277 005__ 20190509132414.0
000170277 0247_ $$2doi$$a10.5075/epfl-thesis-5264
000170277 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis5264-4
000170277 02471 $$2nebis$$a6690826
000170277 037__ $$aTHESIS
000170277 041__ $$aeng
000170277 088__ $$a5264
000170277 245__ $$aDistributed Compressed Representation of Correlated Image Sets
000170277 269__ $$a2012
000170277 260__ $$bEPFL$$c2012$$aLausanne
000170277 300__ $$a174
000170277 336__ $$aTheses
000170277 520__ $$aVision sensor networks and video cameras find widespread  usage in several applications that rely on effective  representation of scenes or analysis of 3D information. These  systems usually acquire multiple images of the same 3D scene  from different viewpoints or at different time instants.  Therefore, these images are generally correlated through  displacement of scene objects. Efficient compression  techniques have to exploit this correlation in order to  efficiently communicate the 3D scene information. Instead of  joint encoding that requires communication between the  cameras, in this thesis we concentrate on distributed  representation, where the captured images are encoded  independently, but decoded jointly to exploit the correlation  between images. One of the most important and challenging  tasks relies in estimation of the underlying correlation from  the compressed correlated images for effective reconstruction  or analysis in the joint decoder. This thesis focuses on developing efficient correlation  estimation algorithms and joint representation of multiple  correlated images captured by various sensing methodologies,  e.g., planar, omnidirectional and compressive sensing (CS)  sensors. The geometry of the 2D visual representation and the  acquisition complexity vary for each sensor type. Therefore,  we need to carefully consider the specific geometric nature  of the captured images while developing distributed  representation algorithms. In this thesis we propose robust  algorithms in different scene analysis and reconstruction  scenarios. We first concentrate on the distributed representation of  omnidirectional images captured by catadioptric sensors. The  omnidirectional images are captured from different viewpoints  and encoded independently with a balanced rate distribution  among the different cameras. They are mapped on the sphere  which captures the plenoptic function in its radial form  without Euclidean discrepancies. We propose a transform-based  distributed coding algorithm, where the spherical images  initially undergo a multi-resolution decomposition. The  visual information is then split into two correlated  partitions. The encoder transmits one partition after entropy  coding, as well as the syndrome bits resulting from the  Slepian-Wolf encoding of the other partition. The joint  decoder estimates a disparity image to take benefit of the  correlation between views and uses the syndrome bits to  decode the missing information. Such a strategy proves to be  beneficial with respect to the independent processing of  images and shows only a small performance loss compared to  the joint encoding of different views. The encoding complexity in the previous approach is  non-negligible due to the visual information processing based  on Slepian-Wolf coding and its associated rate parameter  estimation. We therefore discard the Slepian-Wolf encoding  and propose a distributed coding solution, where the  correlated images are encoded independently using  transform-based coding solutions (e.g., SPIHT). The central  decoder now builds a correlation model from the compressed  images, which is used to jointly decode a pair of images.  Experimental results demonstrate that the proposed  distributed coding solution improves the rate-distortion  performance of the separate coding results for both planar  and omnidirectional images. However, this improvement is  significant only at medium to high bit rates. We therefore  propose a rate allocation scheme that identifies and  transmits the necessary visual information from each image to  improve the correlation estimation accuracy at low bit rate.  Experimental results show that for a given bit budget the  proposed encoding scheme permits to compute an accurate  correlation estimation comparing to the one obtained with  SPIHT, JPEG 2000 or JPEG coding schemes. We show however that  the improvement in the correlation estimation comes at the  price of penalizing the image reconstruction quality;  therefore there exists an interesting trade-off between the  accurate correlation estimation and image reconstruction as  encoding optimization objectives are different in both  cases. Next, we further simplify the encoding complexity by  replacing the classical imaging sensors with the simple CS  sensors, that directly acquire the compressed images in the  form of quantized linear measurements. We now concentrate on  the particular problem, where one image is selected as the  reference and it is used as a side information for the  correlation estimation. We propose a geometry-based model to  describe the correlation between the visual information in a  pair of images. The joint decoder first captures the most  prominent visual features in the reconstructed reference  image using geometric functions. Since the images are  correlated, these features are likely to be present in the  other images too, possibly with geometric transformations.  Hence, we propose to estimate the correlation model with a  regularized optimization problem that locates these features  in the compressed images. The regularization terms enforce  smoothness of the transformation field, and consistency  between the estimated images and the quantized measurements.  Experimental results show that the proposed scheme is able to  efficiently estimate the correlation between images for  several multi-view and video datasets. The proposed scheme is  finally shown to outperform DSC schemes based on unsupervised  disparity (or motion) learning, as well as independent coding  solutions based on JPEG 2000. We then extend the previous scenario to a symmetric  decoding problem, where we are interested to estimate the  correlation model directly from the quantized linear  measurements without explicitly reconstructing the reference  images. We first show that the motion field that represents  the main source of correlation between images can be  described as a linear operator. We further derive a linear  relationship between the correlated measurements in the  compressed domain. We then derive a regularized cost function  to estimate the correlation model directly in the compressed  domain using graph-based optimization algorithms.  Experimental results show that the proposed scheme estimates  an accurate correlation model among images in both multi-view  and video imaging scenarios. We then propose a robust data  fidelity term that improves the quality of the correlation  estimation when the measurements are quantized. Finally, we  show by experiments that the proposed compressed correlation  estimation scheme is able to compete the solution of a scheme  that estimates a correlation model from the reconstructed  images without the complexity of image reconstruction. Finally, we study the benefit of using the correlation  information while jointly reconstructing the images from the  compressed linear measurements. We consider both the  asymmetric and symmetric scenarios described previously. We  propose joint reconstruction methodologies based on a  constrained optimization problem which is solved using  effective proximal splitting methods. The constraints  included in our framework enforce the reconstructed images to  satisfy both the correlation and the quantized measurements  consistency objectives. Experimental results demonstrate that  the proposed joint reconstruction scheme improves the quality  of the decoded images, when compared to a scheme where the  images are handled independently. In this thesis we build efficient distributed scene  representation algorithms for the multiple correlated images  captured in planar, omnidirectional and CS cameras. The  coding rate in our symmetric distributed coding solution  stays balanced between the encoders and stays close to the  joint encoding solutions. Our novel algorithms lead to  effective correlation estimation in different sensing and  coding scenarios. In addition, we provide innovative  solutions for robust correlation estimation from highly  compressed images in simple sensing frameworks. Our CS-based  joint reconstruction frameworks effectively exploit the  inter-view correlation, that permits to achieve high  compression gains compared to state-of-the-art independent  and distributed coding solutions.
000170277 6531_ $$adistributed scene representation
000170277 6531_ $$amulti-view images
000170277 6531_ $$avideo images
000170277 6531_ $$acorrelation estimation
000170277 6531_ $$arandom projections
000170277 6531_ $$ajoint reconstruction
000170277 6531_ $$aquantization
000170277 6531_ $$areprésentation distribuée de scène
000170277 6531_ $$aimage multi-vues
000170277 6531_ $$aimage vidéo
000170277 6531_ $$aestimation de corrélation
000170277 6531_ $$areconstruction conjointe
000170277 6531_ $$aquantification
000170277 700__ $$0242952$$g171237$$aThirumalai, Vijayaraghavan
000170277 720_2 $$aFrossard, Pascal$$edir.$$g101475$$0241061
000170277 8564_ $$uhttps://infoscience.epfl.ch/record/170277/files/EPFL_TH5264.pdf$$zTexte intégral / Full text$$s11586519$$yTexte intégral / Full text
000170277 909C0 $$xU10851$$0252393$$pLTS4
000170277 909CO $$pthesis$$pthesis-bn2018$$pDOI$$ooai:infoscience.tind.io:170277$$qDOI2$$qGLOBAL_SET$$pSTI
000170277 918__ $$dEDEE$$cIEL$$aSTI
000170277 919__ $$aLTS4
000170277 920__ $$b2012
000170277 970__ $$a5264/THESES
000170277 973__ $$sPUBLISHED$$aEPFL
000170277 980__ $$aTHESIS