As a crucial procedure for image stitching, seamline detection has an important impact on the quality of the final mosaic. However, traditional seamline detection methods are less efficient in determining the optimal seamlines for multiple images. This letter presents an efficient superpixel-level seamline detection method for large-scale unmanned aerial vehicle (UAV) image stitching, which can detect the optimal seamlines for hundreds of images in several minutes. Specifically, a novel superpixel-based energy function which simultaneously considers color difference, gradient magnitude, and texture complexity is devised to determine the superpixel-level optimal seamlines in the overlapping areas of multiple images. The energy minimization problem is efficiently solved by the multilabel optimization algorithm. Experimental results on five remote sensing datasets captured by UAVs have shown that the proposed superpixel-level seamline detection method is very effective in generating high-quality seamless mosaics for large-scale images.

Bit-depth enhancement is of significant research interest, and its main challenge lies in eliminating false contours in low-bit images. Existing algorithms either fail to effectively suppress false contours or rely on a vast number of samples and high-performance graphics cards, resulting in poor practicality. To solve these problems, we propose a bilateral false contour elimination filter-based bit-depth enhancement algorithm: BEF-BDE, which contains three innovative modules:Bilateral false contourEliminationFilter (BEF), iterative false contour elimination, and content-adaptive iteration number selection. Experiments on three datasets show that our method can achieve superior subjective and objective results.

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). Because the human visual system (HVS) is the ultimate receiver of visual signals, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel scalable saliency-aware DCVS codec. Firstly, we perform saliency estimation in the the side information (SI) frame generated at the decoder side and adaptively control the size of region-of-interest (ROI) according to the measurements budget by applying a saliency guided foveation model. Subsequently, based on online estimation of the correlation noise between a non-key frame and its SI, we develop a saliency-aware block compressive sensing scheme to more accurately reconstruct the ROI of each non-key frame. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.

By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring the best ways to utilize the spatial/temporal redundancy of video data to achieve efficient sparse representation and reconstruction at the decoder. Unlike the existing DCVS schemes, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel perceptually-aware DCVS codec. Based on online estimation of the correlation noise between a non-key frame and its side information (SI) considering the effect of human visual system (HVS), we design an efficient perceptually-aware block compressive sensing scheme for a non-key frame in our DCVS codec, in order to more accurately reconstruct the salient regions in the video frames. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.