NR-Bitstream video quality metrics for SSIM using encoding decisions in AVC and HEVC coded videos

Lin, Ting-Lan; Yang, Neng-Chieh; Syu, Ray-Hong; Liao, Chin-Chie; Tsai, Wei-Lin; Chou, Chi-Chan; Chen, Shih-Lun

doi:10.1016/j.jvcir.2015.03.008

Cited by 5 publications

(1 citation statement)

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“…It is founded on a top-down assumption that the HVS is highly adapted for extracting structural information from the tantrum, and hence a measure of structural similarity should be a sound estimate of perceived picture quality. It has been discovered that a simple execution of this methodology, namely the structural similarity index can outperform state-of-the-art perceptual image quality metrics [6,[51][52][53][54][55].…”

Section: Structural Similarity Index (Ssim)mentioning

confidence: 99%

Overview of Image Quality Metrics with Perspective to Satellite Image Compression

Sanjith

2016

JERA

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Image Quality appraisal has been an exacting task in the field of image processing without any satisfactory answer so far. Image quality evaluation tries to quantify a visual quality, an amount of distortion in a given picture. These changes are an inescapable component of any digital picture processing. The correct method of valuing the human-perceived visual quality of the images is the assessment by the human beings. Unfortunately, this process is luxurious, time consuming and cannot be applied in real-time applications. Therefore, there is a demand for a computerized technique that would conceive of the human-perceived visual quality as close as possible. This survey presents an overview about different quality metrics used in-order to assess the image degradation. The few metrics studied are MSE, SNR, PSNR, SSIM, AD, MD, MAE, NK, VSNR, RMSE, UIQM, MSSSIM, FSSIM etc. The image quality metrics are verified with perspective to satellite pictures.

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