This paper presents an inter-view motion prediction technique for efficient compression of motion vectors of the depth views in 3D-HEVC. 3D-HEVC is an extension of HEVC standard for coding the multi-view video plus depth content, known as MVD. In MVD format, the motion characteristics of the adjacent views in the depth video are highly correlated. In this paper, we take benefit of this correlation and propose inter-view motion prediction technique, where motion information of the dependent depth views is predicted from the already coded motion information in a reference depth view. In addition, a novel method for deriving disparity vectors based on neighboring pixels is proposed in order to establish correspondences between the blocks in different depth views. Experimental results show that proposed inter-view motion prediction method provides an average bit-rate savings of 1.5% for the synthesized views when the motion information of the depth views is predicted without using the texture information.

This paper presents a novel inter-view motion prediction technique used in 3D-HEVC which provides efficient compression for motion vectors. The 3D extension of HEVC (High Efficiency Video Coding) namely 3D-HEVC is under development in JCT-3V for coding multi-view video and depth data. Inter-view motion prediction takes benefit of the inter-view correlation between views by inferring the motion information of a view from the already coded motion information in another view as well as the local disparity between two views. Experimental results show that inter-view motion prediction in 3D-HEVC provides an average bit-rate savings of 15% for the dependent views.

Distributed representation of correlated multiview images is an important problem that arises in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed images are decoded together in order to take benefit from the image correlation. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG) with a balanced rate distribution among different cameras. A central decoder first estimates the inter-view image correlation from the independently compressed data. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images, which comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be as close as possible to their compressed versions. We show through experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our algorithm compares advantageously to state-of-the-art distributed coding schemes based on motion learning and on the DISCOVER algorithm.

This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or the camera positioning. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model that describes the common information in pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem in order to estimate the corresponding features in correlated images that are given by quantized linear measurements. The correlation model is thus given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that reconstructs the compressed images such that they are consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multiview data sets. In addition, the proposed algorithm provides effective decoding performance that advantageously compares to independent coding solutions and state-of-the-art distributed coding schemes based on disparity learning.

This paper proposes a methodology to estimate the correlation model between a pair of images that are given under the form of linear measurements. We consider an image pair whose common objects are relatively displaced due to the positioning of vision sensors. In such scenarios the correlation model that relates the displacement between the objects is effectively represented by a disparity image. We consider a framework where each image is directly acquired and compressed by projecting onto a random basis of lower dimension. Given the linear measurements computed from the images we propose to estimate the underlying correlation model directly in the compressed domain without reconstructing the images that is usually a costly solution. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship between the images we derive the relationship between the corresponding measurements in the compressed domain. The underlying correlation model is then built by solving a regularized energy minimization problem. Experimental results show that the proposed scheme estimates an accurate correlation model between the images. Also we show by experiments that the proposed scheme performs competitively with the scheme that estimates the correlation model from the reconstructed images.

This paper proposes a joint reconstruction algorithm for compressed correlated images that are given under the form of linear measurements. We consider the particular problem where one image is selected as the reference image and it is used as a side information for decoding the compressed correlated images. These compressed images are given under the form of random measurements that are further quantized and entropy coded. The joint decoder estimates the correlation model based on the geometric transformation of features captured by a structured dictionary. We observe that the high frequency components are not efficiently captured in the estimated image when the correlation information is used alone for image prediction. Hence, we propose a reconstruction strategy that uses the information in the measurements to recover the missing visual information in the predicted image. The reconstruction is based on an optimization algorithm that enforces the reconstructed image to be consistent with the quantized measurements. We further add additional constraints to ensure that the reconstructed image is close to the image predicted from the correlation estimation. The non-linearities introduced due to quantization are considered on both correlation and reconstruction algorithms in order to improve the performance. Experimental results demonstrate the benefit of the reconstruction algorithm as it brings improved coding performance especially at high rate and outperforms independent coding solutions based on JPEG 2000.

This paper presents a novel algorithm for computing the relative motion between images from compressed linear measurements. We propose a geometry based correlation model that describes the relative motion between images by translational motion of visual features. We focus on the problem of estimating the motion field from a reference image and a highly compressed image given by means of random projections, which are further quantized and entropy coded. We capture the most prominent visual features in the reference image using geometric basis functions. Then, we propose a regularized optimization problem for estimating the corresponding features in the compressed image, and eventually the dense motion field is generated from the local transform of the geometric features. Experimental results show that the proposed scheme defines an accurate motion field. In addition, when the motion field is used for image prediction, the resulting rate-distortion (RD) performance becomes better than the independent coding solution based on JPEG-2000, which demonstrates the potential of the proposed scheme for distributed coding algorithms.

This paper presents a novel rate allocation scheme to compute the 3D structure of the scene from compressed stereo images, captured by the distributed vision sensor networks. The images captured at different view points are encoded independently with a balanced rate allocation. The central decoder jointly decodes the information from the encoders, and computes the 3D geometry of the scene in terms of depth map. We first consider the scenario of estimating the 3D geometry from the views, compressed using standard encoders, e.g., SPIHT. Unfortunately, we noticed that the depth value is not precisely reconstructed in the low contrast regions or region around weak edges. It is mainly due to the rate allocation scheme, that allocates the bits based on the variance of the coefficients. We therefore propose a rate allocation scheme, where each encoder first identifies the low contrast regions and then distributes the bits such that the visual information in the low contrast regions is preserved. At the same time, the approximation quality in the rest of the image should not be penalized significantly. We adapt the SPIHT coding scheme to implement the proposed rate allocation methodology. Experimental results show that for a given bit budget, the proposed encoding scheme reconstructs the 3D geometry with more accuracy comparing to SPIHT, JPEG 2000 and JPEG coding schemes.

This paper addresses the problem of compact representation of a 3D scene, captured by distributed omnidirectional cameras. As the images from the sensors are likely to be correlated in most practical scenarios, we build a distributed algorithm based on coding with side information. A reference image is processed with a wavelet transform and progressively encoded. The Wyner-Ziv images undergo a multiresolution representation, and the generated bitplanes are channel encoded with LDPC codes. The central decoder eventually reconstructs the Wyner-Ziv images given by the syndrome bits from the channel codes using the reference omnidirectional image. It also iteratively implements motion estimation on the 2-sphere in order to improve the side information. Experimental results demonstrate that distributed coding improves the rate-distortion performance for coding a set of omnidirectional images when compared to independent coding solutions. The proposed method can further be extended to the decoding of multiple Wyner-Ziv images using one single reference omnidirectional image. Hence, it achieves a reduced overall coding rate compared to disparity-based schemes. In addition, it does not require explicit knowledge of the camera parameters nor precise calibration, which is certainly interesting in camera networks.