Still image/video frame lossy compression providing a desired visual quality

Alexander Zemliachenko, Vladimir Lukin, Nikolay Ponomarenko, Karen Egiazarian, Jaakko Astola

    Research output: Contribution to journalArticleScientificpeer-review

    18 Citations (Scopus)


    The problem of how to automatically provide a desired (required) visual quality in lossy compression of still images and video frames is considered in this paper. The quality can be measured based on different conventional and visual quality metrics. In this paper, we mainly employ human visual system (HVS) based metrics PSNR-HVS-M and MSSIM since both of them take into account several important peculiarities of HVS. To provide a desired visual quality with high accuracy, iterative image compression procedures are proposed and analyzed. An experimental study is performed for a large number of grayscale test images. We demonstrate that there exist several coders for which the number of iterations can be essentially decreased using a reasonable selection of the starting value and the variation interval for the parameter controlling compression (PCC). PCC values attained at the end of the iterative procedure may heavily depend upon the coder used and the complexity of the image. Similarly, the compression ratio also considerably depends on the above factors. We show that for some modern coders that take HVS into consideration it is possible to give practical recommendations on setting a fixed PCC to provide a desired visual quality in a non-iterative manner. The case when original images are corrupted by visible noise is also briefly studied.

    Original languageEnglish
    Pages (from-to)697-718
    JournalMultidimensional Systems and Signal Processing
    Issue number3
    Publication statusPublished - 2016
    Publication typeA1 Journal article-refereed


    • Compression ratio
    • Lossy compression
    • Required quality
    • Visual quality metrics

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems
    • Signal Processing
    • Software
    • Artificial Intelligence
    • Hardware and Architecture
    • Applied Mathematics


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