Video error concealment: a brief presentation

Typical error control techniques are not very well suited for video transmission. On the other hand, video transmission over error prone channels has increased greatly, e.g., over IP and wireless networks. These two facts combined together provided the necessary motivation for the development of a new set of techniques (error concealment) capable of dealing with transmission errors in video systems. These techniques can be categorized according with the approach they take to solve the problem. This categorization is presented, describing the assumptions in which they are based and giving a few examples in each category. Finally, the advantages and disadvantages of each category are presented

Side-information generation for temporally and spatially scalablewyner-ziv codecs

The distributed video coding paradigmenables video codecs to operate with reversed complexity, in which the complexity is shifted from the encoder toward the decoder. Its performance is heavily dependent on the quality of the side information generated by motio estimation at the decoder. We compare the rate-distortion performance of different side-information estimators, for both temporally and spatially scalableWyner-Ziv codecs. For the temporally scalable codec we compared an established method with a new algorithm that uses a linear-motion model to produce side-information. As a continuation of previous works, in this paper, we propose to use a super-resolution method to upsample the nonkey frame, for the spatial scalable codec, using the key frames as reference.We verify the performance of the spatial scalableWZcoding using the state-of-the-art video coding standard H.264/AVC

10411 Abstracts Collection -- Computational Video

From 10.10.2010 to 15.10.2010, the Dagstuhl Seminar 10411 ``Computational Video \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method

The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment. However, they typically simulate the missing content by manual-designed error masks, thus failing to fill in the realistic video loss in video communication (e.g., telepresence, live streaming, and internet video) and multimedia forensics. To address this, we introduce the bitstream-corrupted video (BSCV) benchmark, the first benchmark dataset with more than 28,000 video clips, which can be used for bitstream-corrupted video recovery in the real world. The BSCV is a collection of 1) a proposed three-parameter corruption model for video bitstream, 2) a large-scale dataset containing rich error patterns, multiple corruption levels, and flexible dataset branches, and 3) a plug-and-play module in video recovery framework that serves as a benchmark. We evaluate state-of-the-art video inpainting methods on the BSCV dataset, demonstrating existing approaches' limitations and our framework's advantages in solving the bitstream-corrupted video recovery problem. The benchmark and dataset are released at https://github.com/LIUTIGHE/BSCV-Dataset.Comment: Accepted by NeurIPS Dataset and Benchmark Track 202

Total Directional Variation for Video Denoising

In this paper, we propose a variational approach for video denoising, based on a total directional variation (TDV) regulariser proposed in Parisotto et al. (2018), for image denoising and interpolation. In the TDV regulariser, the underlying image structure is encoded by means of weighted derivatives so as to enhance the anisotropic structures in images, e.g. stripes or curves with a dominant local directionality. For the extension of TDV to video denoising, the space-time structure is captured by the volumetric structure tensor guiding the smoothing process. We discuss this and present our whole video denoising work-flow. Our numerical results are compared with some state-of-the-art video denoising methods.SP acknowledges UK EPSRC grant EP/L016516/1 for the CCA DTC. CBS acknowledges support from Leverhulme Trust project on Breaking the non-convexity barrier, EPSRC grant Nr. EP/M00483X/1, the EPSRC Centre EP/N014588/1, the RISE projects CHiPS and NoMADS, the CCIMI and the Alan Turing Institute

Parallel HEVC Decoding on Multi- and Many-core Architectures : A Power and Performance Analysis

The Joint Collaborative Team on Video Decoding is developing a new standard named High Efficiency Video Coding (HEVC) that aims at reducing the bitrate of H.264/AVC by another 50 %. In order to fulfill the computational demands of the new standard, in particular for high resolutions and at low power budgets, exploiting parallelism is no longer an option but a requirement. Therefore, HEVC includes several coding tools that allows to divide each picture into several partitions that can be processed in parallel, without degrading the quality nor the bitrate. In this paper we adapt one of these approaches, the Wavefront Parallel Processing (WPP) coding, and show how it can be implemented on multi- and many-core processors. Our approach, named Overlapped Wavefront (OWF), processes several partitions as well as several pictures in parallel. This has the advantage that the amount of (thread-level) parallelism stays constant during execution. In addition, performance and power results are provided for three platforms: a server Intel CPU with 8 cores, a laptop Intel CPU with 4 cores, and a TILE-Gx36 with 36 cores from Tilera. The results show that our parallel HEVC decoder is capable of achieving an average frame rate of 116 fps for 4k resolution on a standard multicore CPU. The results also demonstrate that exploiting more parallelism by increasing the number of cores can improve the energy efficiency measured in terms of Joules per frame substantially