31,400 research outputs found

    No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

    Get PDF
    Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives

    An Initial study on The Reliability of Power Semiconductor Devices

    Get PDF
    An initial literature study combined with some basic comparative simulations has been performed on different electricfield modulation techniques and the subsequent reliability issues are reported for power semiconductor devices. An explanation of the most important power device metrics such as the offstate breakdown (BV) and specific on-resistance (RON) will be given, followed by a short overview of some of the electrostatic techniques (fieldplates, RESURF e.g. [1]) used to suppress peak electric fields. Furthermore it will be addressed that the high current operation of these devices results in shifting electric field peaks (Kirk effect [2], [3]) and as such different avalanche behavior, resulting in (gate oxide) reliability issues unlike those of conventional CMOS

    Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution

    Full text link
    Incomplete color sampling, noise degradation, and limited resolution are the three key problems that are unavoidable in modern camera systems. Demosaicing (DM), denoising (DN), and super-resolution (SR) are core components in a digital image processing pipeline to overcome the three problems above, respectively. Although each of these problems has been studied actively, the mixture problem of DM, DN, and SR, which is a higher practical value, lacks enough attention. Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM \to DN \to SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality. In this paper, we rethink the mixture problem from a holistic perspective and propose a new image processing pipeline: DN \to SR \to DM. Extensive experiments show that simply modifying the usual sequential solution by leveraging our proposed pipeline could enhance the image quality by a large margin. We further adopt the proposed pipeline into an end-to-end network, and present Trinity Enhancement Network (TENet). Quantitative and qualitative experiments demonstrate the superiority of our TENet to the state-of-the-art. Besides, we notice the literature lacks a full color sampled dataset. To this end, we contribute a new high-quality full color sampled real-world dataset, namely PixelShift200. Our experiments show the benefit of the proposed PixelShift200 dataset for raw image processing.Comment: Code is available at: https://github.com/guochengqian/TENe
    corecore