A new reference frame recompression algorithm and its VLSI architecture for UHDTV video codec

Li Guo, Dajiang Zhou, Satoshi Goto

    Research output: Contribution to journalArticle

    28 Citations (Scopus)

    Abstract

    Video encoders and decoders for HEVC-like compression standards require huge external memory bandwidth, which occupies a significant portion of the codec power consumption. To reduce the memory bandwidth, this paper presents a new lossless reference frame recompression algorithm along with a high-throughput hardware architecture. Firstly, hybrid spatial-domain prediction is proposed to combine the merits of DPCM scanning and averaging. The prediction is then enhanced with multiple modes to accommodate various image characteristics. Finally, efficient residual regrouping based on semi-fixed-length (SFL) coding is used to improve the compression performance. Compared to no compression, the proposed scheme can reduce data traffic by an average of 57.6% with no image quality degradation. The average compression ratio is 2.49, an improvement of at least 12.2-13.2%, relative to the state-of-the-art algorithms. By applying a reordered two-step architecture and the two optimizations, residual reuse and simplified coding mode decision, the hardware cost is similar to that of previous reference frame recompression architectures. The computational complexity increase caused by multi-mode prediction affects the HW cost slightly. This work can be implemented with 45.1 k gates for the compressor and 34.5 k gates for the decompressor at 300 MHz, enough to support a 3840 x 2160@60fps video encoder and decoder.

    Original languageEnglish
    Article number6881682
    Pages (from-to)2323-2332
    Number of pages10
    JournalIEEE Transactions on Multimedia
    Volume16
    Issue number8
    DOIs
    Publication statusPublished - 2014 Dec 1

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    Keywords

    • Embedded compression
    • H.264/AVC
    • HEVC
    • Lossless reference frame recompression
    • Multi-mode DPCM and averaging prediction

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Signal Processing
    • Media Technology
    • Computer Science Applications

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