Error-resilient performance of Dirac video codec over packet-erasure channel

Video transmission over the wireless or wired network requires error-resilient mechanism since compressed video bitstreams are sensitive to transmission errors because of the use of predictive coding and variable length coding. This paper investigates the performance of a simple and low complexity error-resilient coding scheme which combines source and channel coding to protect compressed bitstream of wavelet-based Dirac video codec in the packet-erasure channel. By partitioning the wavelet transform coefficients of the motion-compensated residual frame into groups and independently processing each group using arithmetic and Forward Error Correction (FEC) coding, Dirac could achieves the robustness to transmission errors by giving the video quality which is gracefully decreasing over a range of packet loss rates up to 30% when compared with conventional FEC only methods. Simulation results also show that the proposed scheme using multiple partitions can achieve up to 10 dB PSNR gain over its existing un-partitioned format. This paper also investigates the error-resilient performance of the proposed scheme in comparison with H.264 over packet-erasure channel

Role of homeostasis in learning sparse representations

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components

A Research and Strategy of Remote Sensing Image Denoising Algorithms

Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed results to the ground. Image processing is the main data processing on satellites, in this paper, we focus on image denoising which is the basic image processing. There are many high-performance denoising approaches at present, however, most of them rely on advanced computing resources or rich images on the ground. Considering the limited computing resources of satellites and the characteristics of remote sensing images, we do some research on these high-performance ground image denoising approaches and compare them in simulation experiments to analyze whether they are suitable for satellites. According to the analysis results, we propose two feasible image denoising strategies for satellites based on satellite TianZhi-1.Comment: 9 pages, 4 figures, ICNC-FSKD 201

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio