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Video Coding Based on SSIM
Content Provider | Semantic Scholar |
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Copyright Year | 2014 |
Abstract | This paper proposes a new image quality assessment algorithmstructured similarity index (SSIM) for video coding applications. It mainly incorporates the human visual sensitivity characteristics for video coding. This method can achieve significant gain at lower bit rate in terms of rate SSIM performance. Similarity measure is based on three comparisons such as luminance, chrominance and structure. Structural comparison extracts structural characteristics of an image which can improves the quality of video. Better quality video can be achieved by varying the value of quantisation parameter. Experimental results show the variation of video quality at different quantisation parameters. Keywords— Video coding, Structured Similarity Index, Peak Signal to Noise Ratio, Mean Square Error |
File Format | PDF HTM / HTML |
Alternate Webpage(s) | http://www.ijettjournal.org/volume-10/number-10/IJETT-V10P300.pdf |
Language | English |
Access Restriction | Open |
Subject Keyword | Data compression Error detection and correction Image quality Mean squared error Population Parameter Quantization (image processing) Quantization (physics) Signal-to-noise ratio Similarity measure Structural similarity Video |
Content Type | Text |
Resource Type | Article |