9,798 research outputs found

    Generic techniques to improve SVC enhancement layer encoding: digest of technical papers

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    Scalable video coding is an important mechanism to provide different types of end-user devices with different versions of the same encoded bitstream. However, scalable video encoding remains a computationally expensive operation. To decrease the complexity we propose generic techniques. These techniques can also be combined with existing fast mode decision modes. We show that extending these existing techniques yield an average complexity reduction of 87%

    Scalable video transcoding for mobile communications

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    Mobile multimedia contents have been introduced in the market and their demand is growing every day due to the increasing number of mobile devices and the possibility to watch them at any moment in any place. These multimedia contents are delivered over different networks that are visualized in mobile terminals with heterogeneous characteristics. To ensure a continuous high quality it is desirable that this multimedia content can be adapted on-the-fly to the transmission constraints and the characteristics of the mobile devices. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a technique to convert an H.264/AVC bitstream without scalability to a scalable bitstream with temporal scalability as part of a scalable video transcoder for mobile communications. The results show that when our technique is applied, the complexity is reduced by 98 % while maintaining coding efficiency

    Combining open- and closed-loop architectures for H.264/AVC-TO-SVC transcoding

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    Scalable video coding (SVC) allows encoded bitstreams to be adapted. However, most bitstreams do not incorporate this scalability so bitstreams have to be adapted multiple times to accommodate for varying network conditions or end-user devices. Each adaptation incorporates an additional loss of quality due to transcoding. To overcome this issue, we propose a single transcoding step from H.264/AVC to SVC. Doing so, the resulting bitstream can be freely adapted without any additional quality reduction. Open-loop transcoding architectures can be used for H.264/AVC-to-SVC transcoding with a low complexity, although these architectures suffer from drift artifacts. Closed-loop transcoding, on the other hand, requires a higher complexity. To overcome the drawbacks of both systems, we propose combining both techniques

    Temporal video transcoding from H.264/AVC-to-SVC for digital TV broadcasting

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    Mobile digital TV environments demand flexible video compression like scalable video coding (SVC) because of varying bandwidths and devices. Since existing infrastructures highly rely on H.264/AVC video compression, network providers could adapt the current H.264/AVC encoded video to SVC. This adaptation needs to be done efficiently to reduce processing power and operational cost. This paper proposes two techniques to convert an H.264/AVC bitstream in Baseline (P-pictures based) and Main Profile (B-pictures based) without scalability to a scalable bitstream with temporal scalability as part of a framework for low-complexity video adaptation for digital TV broadcasting. Our approaches are based on accelerating the interprediction, focusing on reducing the coding complexity of mode decision and motion estimation tasks of the encoder stage by using information available after the H. 264/AVC decoding stage. The results show that when our techniques are applied, the complexity is reduced by 98 % while maintaining coding efficiency

    On the impact of the GOP size in a temporal H.264/AVC-to-SVC transcoder in baseline and main profile

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    Scalable video coding is a recent extension of the advanced video coding H.264/AVC standard developed jointly by ISO/IEC and ITU-T, which allows adapting the bitstream easily by dropping parts of it named layers. This adaptation makes it possible for a single bitstream to meet the requirements for reliable delivery of video to diverse clients over heterogeneous networks using temporal, spatial or quality scalability, combined or separately. Since the scalable video coding design requires scalability to be provided at the encoder side, existing content cannot benefit from it. Efficient techniques for converting contents without scalability to a scalable format are desirable. In this paper, an approach for temporal scalability transcoding from H.264/AVC to scalable video coding in baseline and main profile is presented and the impact of the GOP size is analyzed. Independently of the GOP size chosen, time savings of around 63 % for baseline profile and 60 % for main profile are achieved while maintaining the coding efficiency

    Complexity Analysis Of Next-Generation VVC Encoding and Decoding

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    While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.Comment: IEEE ICIP 202

    Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

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    Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

    Fast Implementation of the Scalable Video Coding Extension of the H.264/AVC Standard

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    In order to improve coding efficiency in the scalable extension of H.264/AVC, an inter-layer prediction mechanism is incorporated. This exploits as much lower layer information as possible to inform the process of coding the enhancement layer(s). However it also greatly increases the computational complexity. In this paper, a fast mode decision algorithm for efficient implementation of the SVC encoder is described. The proposed algorithm not only considers inter-layer correlation but also fully exploits both spatial and temporal correlation as well as an assessment of macroblock texture. All of these factors are organised within a hierarchical structure in the mode decision process. At each level of the structure, different strategies are implemented to eliminate inappropriate candidate modes. Simulation results show that the proposed algorithm reduces encoding time by up to 85% compared with the JSVM 9.18 implementation. This is achieved without any noticeable degradation in rate distortion
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