Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames.

Depth maps are becoming increasingly important in the context of emerging video coding and processing applications. Depth images represent the scene surface and are characterized by areas of smoothly varying grey levels separated by sharp edges at the position of object boundaries. To enable high quality view rendering at the receiver side, preservation of these characteristics is important. Lossless coding enables avoiding rendering artifacts in synthesized views due to depth compression artifacts. In this paper, we propose a binary tree based lossless depth coding scheme that arranges the residual frame into integer or binary residual bitmap. High spatial correlation in depth residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmetic coding. On the standard 3D video sequences, the proposed lossless depth coding has achieved compression ratio in the range of 20 to 80.

Motion vectors of depth-maps in multiview and free-viewpoint videos exhibit strong spatial as well as inter-component clustering tendency. This paper presents a novel motion vector coding technique that first compresses the multidimensional bitmaps of macro block mode information and then encodes only the non-zero components of motion vectors. The bitmaps are partitioned into disjoint cuboids using binary tree based decomposition so that the 0's and 1's are either highly polarized or further sub-partitioning is unlikely to achieve any compression. Each cuboid is entropy-coded as a unit using binary arithmetic coding. This technique is capable of exploiting the spatial and inter-component correlations efficiently without the restriction of scanning the bitmap in any specific linear order as needed by run-length coding. As encoding of non-zero component values no longer requires denoting the zero value, further compression efficiency is achieved. Experimental results on standard multiview test video sequences have comprehensively demonstrated the superiority of the proposed technique, achieving overall coding gain against the state-of-the-art in the range [17%,51%] and on average 31%.

A key goal in transportation system is to attain efficient road traffic through minimization of trip time, fuel consumption and pollutant-emission without compromising safety. In dense traffic lane-changes and merging are often key ingredients to cause safety hazards, traffic breakdowns and travel delays. In this paper, we propose an efficient cooperative lane-changing algorithm CLA for sensor- and communication-enabled automated vehicles to reduce the lane-changing bottlenecks. For discretionary lane-changing, we consider the advantages of the subject vehicle, the follower in the current lane and k (an integer) lag vehicles in the target lane to maximize speed gains. Our algorithm simultaneously minimizes the impact of lane-change on traffic flow and the overall trip time, fuel-consumption and pollutant-emission. For mandatory lane-changing CLA dissociates the decision-making point from the actual mandatory lane-changing point and computes a suitable lane-changing slot in order to minimize lane-changing (merging) time. Our algorithm outperforms the potential cooperative lane-changing algorithm MOBIL proposed by Kesting et al. [1] in terms of merging time and rate, waiting time, fuel consumption, average velocity and flow (especially at the point in front of the merging point) at the cost of slightly increased average trip time for the mainroad vehicles compared to MOBIL. We also highlight important directions for further research.

State-of-the-art lossless image compression schemes, such as, JPEG-LS and CALIC, have been proposed in the context adaptive predictive coding framework. These schemes involve a prediction step followed by context adaptive entropy coding of the residuals. It can be observed that there exist significant spatial correlation among the residuals after prediction. The efficient schemes proposed in the literature rely on context adaptive entropy coding to exploit this spatial correlation. In this paper, we propose an alternative approach to exploit this spatial correlation. The proposed scheme also involves a prediction stage. However, we resort to a binary tree based hierarchical decomposition technique to efficiently exploit the spatial correlation. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall compression gain of 2.1% against JPEG-LS.

Motion vectors of depth-maps in multiview and free-viewpoint videos exhibit strong spatial as well as inter-component clustering tendency. This paper presents a novel coding technique that first compresses the multidimensional bitmaps of macroblock mode and then encodes only the non-zero components of motion vectors. The bitmaps are partitioned into disjoint cuboids using binary tree based decomposition so that the 0's and 1's are either highly polarized or further sub-partitioning is unlikely to achieve any compression. Each cuboid is entropy-coded as a unit using binary arithmetic coding. This technique is capable of exploiting the spatial and inter-component correlations efficiently without the restriction of scanning the bitmap in any specific linear order as needed by run-length coding. As encoding of non-zero component values no longer requires denoting the zero value, further compression efficiency is achieved. Experimental results on standard multiview test video sequences have comprehensively demonstrated the superiority of the proposed technique, achieving overall coding gain against the state-of-the-art in the range [22%, 54%] and on average 38%.

Video coding techniques utilising background frames, provide better rate distortion performance by exploiting coding efficiency in uncovered background areas compared to the latest video coding standard. Parametric approaches such as the mixture of Gaussian (MoG) based background modeling has been widely used however they require prior knowledge about the test videos for parameter estimation. Recently introduced non-parametric (NP) based background modeling techniques successfully improved video coding performance through a HEVC integrated coding scheme. The inherent nature of the NP technique naturally exhibits superior performance in dynamic background scenarios compared to the MoG based technique without a priori knowledge of video data distribution. Although NP based coding schemes showed promising coding performances, they suffer from a number of key challenges - (a) determination of the optimal subset of training frames for generating a suitable background that can be used as a reference frame during coding, (b) incorporating dynamic changes in the background effectively after the initial background frame is generated, (c) managing frequent scene change leading to performance degradation, and (d) optimizing coding quality ratio between an I-frame and other frames under bit rate constraints. In this study we develop a new scene adaptive coding scheme using the NP based technique, capable of solving the current challenges by incorporating a new continuously updating background generation process. Extensive experimental results are also provided to validate the effectiveness of the new scheme.

In this paper, we have extended the Rice-Golomb code so that it can operate at fractional precision to efficiently exploit the real-valued predictions. Coding at infinitesimal precision allows the residuals to be modeled with the Lap lace distribution. Unlike the Rice-Golomb code, which maps equally probable opposite-signed residuals to different integers, the proposed coding scheme is symmetric in the sense that, at infinitesimal precision, it assigns code words of equal length to equally probable residual intervals. The symmetry of both the Lap lace distribution and the coding scheme facilitates the analysis of the proposed coding scheme to determine the average code-length and the optimal value of the associated coding parameter.

In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coefficients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coefficient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM.

Minimal list decoding for a code C refers to list decoding with radius L(y), where L(y) is the minimum of the distances between the received word y and any codeword in C. In this paper we present a minimal list decoding algorithm for Reed-Solomon (RS) codes. Our approach involves a parametrization of the interpolating polynomials of a minimal Gröbner basis G. We then demonstrate that our parametric approach can be solved by a computationally efficient rational curve fitting solution from a recent paper by Wu. Besides, we present an algorithm to compute the minimum multiplicity as well as the associated optimal values of the parameters. Use of these optimal parameters in the rational interpolation step results in computational as well as memory efficiency.

In this paper, we present a minimal list decoding algorithm for Reed-Solomon (RS) codes. Minimal list decoding for a code C refers to list decoding with radius L, where L is the minimum of the distances between the received word r and any codeword in C. We consider the problem of determining the value of L as well as determining all the codewords at distance L. Our approach involves a parametrization of interpolating polynomials of a minimal Gröbner basis G . We present two efficient ways to compute G. We also show that so-called re-encoding can be used to further reduce the complexity. We then demonstrate how our parametric approach can be solved by a computationally feasible rational curve fitting solution from a recent paper by Wu. Besides, we present an algorithm to compute the minimum multiplicity as well as the optimal values of the parameters associated with this multiplicity, which results in overall savings in both memory and computation.

A block-based lossless video coding scheme using the notion of binning has been proposed in. To further improve the compression and reduce the complexity, in this paper we investigate the impact of two sub-optimal motion search algorithms on the performance of this lattice-based scheme. While one of the algorithm tries avoiding motion vectors, the other tries to reduce complexity. Our experimental results have demonstrated that the loss due to sub-optimal motion search outweighs the gain when motion vectors are avoided. However, experimental results have shown that there is negligible performance loss when low-complexity sub-optimal three step search is used.

Existing literature on source coding with side information (SCSI) uses channel codes like LDPC codes and turbo codes and assumes classical unique decoding. In this paper, in contrast to classical decoding, we have taken the list decoding approach and show that the theoretical limit of SCSI can then be achieved. We argue that, as opposed to channel coding, the correct sequence from the list produced by the list decoder can effectively be recovered in case of SCSI with a few CRC bits. The CRC bits, which allow the decoder to identify the correct sequence, incur negligible overhead for large block length. More importantly, these CRC bits are not subject to noise since we are dealing with a virtual noisy channel rather than a real noisy channel. Finally, we present a guideline for designing constructive SCSI schemes using Reed Solomon code, BCH code, and Reed-Muller code, which are the known list-decodable codes.

Due to the infrequent medium access in Wireless Sensor Networks (WSN), their MAC protocols are mostly based on CSMA. In this paper we present an efficient contention resolution scheme for CSMA based MAC protocols which is suitable for periodic data collection in WSNs. Taking into account that the number of nodes in a single cluster is fixed, this protocol uses successively decreasing contention window. It is characterized by non-overlapping contention window, that maintains a constant successful transmission rate. It significantly decreases data collection time by minimizing the time wastage due to collisions. At the same time, by using adaptive CW, it reduces the time wastage in empty slots. Experimental results demonstrate that in periodic data collection within a single hop cluster this scheme has performance superior to the recently proposed Synchronous Shared Contention Window (SSCW) based scheme in terms of time wastage and throughput.

Information theoretic proof exists to support that independent encoding of distributed sources with a joint decoder by exploiting the correlation among the sources can be as efficient as encoding them jointly. This paper successfully attempts to apply this concept for lossless video coding using lattices to divide the multidimensional integer pixel intensity hyperspace of a block of pixels into a finite number of cosets and encoding each block with its coset index. This radical departure from conventional predictive coding techniques not only offers very low computational complexity by avoiding expensive learning of predictor coefficients but also avoids any coding loss due to rounding of real-valued predictions. On standard test video sequences, this scheme achieved compression within as low as 4.6% of the latest scheme with optimal learning of predictor coefficients. The former, however, outperformed the latter as soon as it started updating the optimal predictor coefficients less frequently to reduce the computational complexity.