In this paper, the Relative Pose based Redundancy Removal (RPRR) scheme is presented, which has been designed for mobile RGB-D sensor networks operating under bandwidth-constrained operational scenarios. The scheme considers a multiview scenario in which pairs of sensors observe the same scene from different viewpoints, and detect the redundant visual and depth information to prevent their transmission leading to a significant improvement in wireless channel usage efficiency and power savings. We envisage applications in which the environment is static, and rapid 3D mapping of an enclosed area of interest is required, such as disaster recovery and support operations after earthquakes or industrial accidents. Experimental results show that wireless channel utilization is improved by 250 and battery consumption is halved when the RPRR scheme is used instead of sending the sensor images independently.
The collaborative compression and transmission methods @cite_33 @cite_70 @cite_58 @cite_76 @cite_24 jointly encode the captured multi-view images. The spatial correlation is explored and removed at encoders by image registration algorithms. Only the uncorrelated visual content is delivered in the network after being jointly encoded by some recent coding techniques (e.g., Multiview Video Coding (MVC) @cite_14 @cite_79 ) and compressive sensing approaches @cite_39 @cite_2 . However, at least one node in the network is required to have the full set of images captured by the other sensors in order to perform image registration. This means that the redundant information cannot be removed completely and still needs to be transmitted at least once. Moreover, as color images do not contain a full 3-D representation of a scene, these methods introduce distortions and errors when the relative poses (location and orientation) between sensors are not pure rotation or translation, or the scenes have complex geometrical structures and occlusions.
@cite_14 @cite_33 @cite_70 @cite_58 @cite_39 @cite_24 @cite_79 @cite_2 @cite_76
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In this article, a multiview image compression framework, which involves the use of Block-based Compressive Sensing (BCS) and Joint Multiphase Decoding (JMD), is proposed for a Visual Sensor Network (VSN). In the proposed framework, one of the sensor nodes is configured to serve as the reference node, the others as nonreference nodes. The images are encoded independently using the BCS to produce two observed measurements that are transmitted to the host workstation. In this case, the nonreference nodes always encoded the images (INR) at a lower subrate when compared with the images from the reference nodes (IR). The idea is to improve the reconstruction of INR using IR. After the two observed measurements are received by the host workstation, they are first decoded independently, then image registration is applied to align IR onto the same plane of INR. The aligned IR is then fused with INR, using wavelets to produce the projected image IP. Subsequently, the difference between the measurements of the IP and INR is calculated. The difference is then decoded and added to IP to produce the final reconstructed INR. The simulation results show that the proposed framework is able to improve the quality of INR on average by 2dB to 3dB at lower subrates when compared with other Compressive Sensing (CS)--based multiview image compression frameworks. This paper proposes a lenslet image compression method scalable from low bitrates to fully lossless. The subaperture images are split into two sets: a set of reference views, encoded by a standard lossy or lossless compressor, and the set of dependent views, which are reconstructed by sparse prediction from the reference set using the geometrical information from the depth map. The set of reference views may contain all views and all views may also be dependent views, in which case the sparse predictive stage does not reconstruct from scratch the views, but it refines in a sequential order all views by combining in an optimal way the information about the same region existing in neighbor views. The encoder transmits to the decoder a segmented version of the scene depthmap, the encoded versions of the reference views, displacements for each region from the central view to each of the dependent views, and finally the sparse predictors for each region and each dependent view. The scheme can be configured to ensure random access to the dependent views, while the reference views are compressed in a backward compatible way, e.g., using JPEG 2000. The experimental results show performance better than that of the baseline standard compressor used, JPEG 2000. A major fraction of multimedia stream contents tends to be redundant and leads to wastage of storage capacity and channel bandwidth. In order to eliminate surplus data, standard video compression algorithms exploit spatial and temporal correlation present in video sequence. However, in case of a multisensor network, intersensor statistical redundancy is the most significant factor in acquiring efficient link utilization as well as making resultant findings valuable to the end user. In this paper, an extension to our previously proposed scheme has been presented to accomplish performance goals of a multisensor environment. Standard MPEG codec has been used to accomplish distributed motion compensation in prespecified directions known as directional correlation. Video frame correlation has been estimated locally at the camera node as well as across different nodes, defined as node communication strategies. Further, receiver feedback assists in quality control after reconstitution by decoder assessment. Results estimated have been analyzed for saving ratios and multimedia quality. Results analysis illustrates increased gains in frame quality and compression saving, achieved through reducing node displacement from the reference node (NR). In this paper, we design a novel multiview video encoding decoding architecture for wirelessly multiview video streaming applications, e.g., 360 degrees video, Internet of Things (IoT) multimedia sensing, among others, based on distributed video coding and compressed sensing principles. Specifically, we focus on joint decoding of independently encoded compressively sampled multiview video streams. We first propose a novel side-information (SI) generation method based on a new interview motion compensation algorithm for multiview video joint reconstruction at the decoder end. Then, we propose a technique to fuse the received measurements with resampled measurements from the generated SI to perform the final recovery. Based on the proposed joint reconstruction method, we also derive a blind video quality estimation technique that can be used to adapt online the video encoding rate at the sensors to guarantee desired quality levels in multiview video streaming. Extensive simulation results of real multiview video traces show the effectiveness of the proposed fusion reconstruction method with the assistance of SI generated by an interview motion compensation method. Moreover, they also illustrate that the blind quality estimation algorithm can accurately estimate the reconstruction quality.