Measurement of visual quality is of fundamental importance to many image and video processing applications. Traditionally, quality assessment metrics predict visual quality following the paradigm of modeling the known properties of the Human Visual System (HVS). The Structural Similarity (SSIM) index is a new approach which assumes that the HVS is highly adapted for extracting structural information from a scene and has been proved outperforms PSNR and many state-of-the-art HVS-based metrics. In this paper, we present a new metric for video quality assessment which selects local sampling regions according to their edginess and uses the SSIM index as local distortion measurement. We validate the performance of our metric by testing it on the VQEG Phase I dataset, and showed that our metric has a good correlation with perceived visual quality.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Video Quality Assessment Based on Edge Structural Similarity


    Contributors:
    Ye, Shengnan (author) / Su, Kaina (author) / Xiao, Chuangbai (author)


    Publication date :

    2008-05-01


    Size :

    285888 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Method of image quality assessment based on human visual system and structural similarity

    Wei, Y. / Yan, Z. / Dong, X. | British Library Online Contents | 2008


    Video Quality Assessment

    Fowdur, Tulsi Pawan / Babooram, Lavesh | Springer Verlag | 2024


    A Fast Similarity-based Video Retrieval

    Cao, Z. / Lu, B. / Zhu, M. | British Library Online Contents | 2010


    Self-similarity-based image edge detection [4925-46]

    Zhang, Z. B. / Society of Photo-optical Instrumentation Engineers / Chinese Optical Society | British Library Conference Proceedings | 2002


    Structural Quality Assessment of Edge Block Image Steganography with Improved Data Capacity

    Singh, Ratnesh Kumar / Singh, Preeti / Gaurav, Kumar | IEEE | 2018