Digital Watermarking Based Image and Video Quality Evaluation
Image and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Referenc...
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ndltd-LACETR-oai-collectionscanada.gc.ca-OOU-OLD.-239882013-04-05T03:22:00ZDigital Watermarking Based Image and Video Quality EvaluationWang, ShaWatermarking based image and video quality evaluationadaptive watermarkingsemi-fragile watermarkingDWT based watermark embeddingHVS maskingImage and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Reference quality metric, which estimates signal quality by assessing the degradation of the embedded watermark. Since the watermark contains a small amount of information compared to the cover signal, performing accurate signal quality evaluation is a challenging task. Meanwhile, the watermarking process causes signal quality loss. To address these problems, in this thesis, a framework for image and video quality evaluation is proposed based on semi-fragile and adaptive watermarking. In this framework, adaptive watermark embedding strength is assigned by examining the signal quality degradation characteristics. The "Ideal Mapping Curve" is experimentally generated to relate watermark degradation to signal degradation so that the watermark degradation can be used to estimate the quality of distorted signals. With the proposed framework, a quantization based scheme is first implemented in DWT domain. In this scheme, the adaptive watermark embedding strengths are optimized by iteratively testing the image degradation characteristics under JPEG compression. This iterative process provides high accuracy for quality evaluation. However, it results in relatively high computational complexity. As an improvement, a tree structure based scheme is proposed to assign adaptive watermark embedding strengths by pre-estimating the signal degradation characteristics, which greatly improves the computational efficiency. The SPIHT tree structure and HVS masking are used to guide the watermark embedding, which greatly reduces the signal quality loss caused by watermark embedding. Experimental results show that the tree structure based scheme can evaluate image and video quality with high accuracy in terms of PSNR, wPSNR, JND, SSIM and VIF under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering, Gaussian noise distortion, H.264 compression and packet loss related distortion.2013-04-02T20:27:23Z2013-04-02T20:27:23Z20132013-04-02Thèse / Thesishttp://hdl.handle.net/10393/23988en |
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Watermarking based image and video quality evaluation adaptive watermarking semi-fragile watermarking DWT based watermark embedding HVS masking |
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Watermarking based image and video quality evaluation adaptive watermarking semi-fragile watermarking DWT based watermark embedding HVS masking Wang, Sha Digital Watermarking Based Image and Video Quality Evaluation |
description |
Image and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Reference quality
metric, which estimates signal quality by assessing the degradation of the embedded watermark. Since the watermark contains a small
amount of information compared to the cover signal, performing accurate signal quality evaluation is a challenging task. Meanwhile,
the watermarking process causes signal quality loss.
To address these problems, in this thesis, a framework for image and video quality evaluation is proposed based on semi-fragile and adaptive watermarking. In this framework, adaptive watermark embedding strength is assigned by examining the signal quality
degradation characteristics. The "Ideal Mapping Curve" is experimentally generated to relate watermark degradation to signal
degradation so that the watermark degradation can be used to estimate the quality of distorted signals.
With the proposed framework, a quantization based scheme is first implemented in DWT domain. In this scheme, the adaptive watermark
embedding strengths are optimized by iteratively testing the image degradation characteristics under JPEG compression. This iterative process provides high accuracy for quality evaluation. However, it results in relatively high computational complexity.
As an improvement, a tree structure based scheme is proposed to assign adaptive watermark embedding strengths by pre-estimating the signal degradation characteristics, which greatly improves the
computational efficiency. The SPIHT tree structure and HVS masking are used to guide the watermark embedding, which greatly reduces the signal quality loss caused by watermark embedding. Experimental results show that the tree structure based scheme can evaluate image
and video quality with high accuracy in terms of PSNR, wPSNR, JND, SSIM and VIF under JPEG compression, JPEG2000 compression, Gaussian
low-pass filtering, Gaussian noise distortion, H.264 compression and packet loss related distortion. |
author |
Wang, Sha |
author_facet |
Wang, Sha |
author_sort |
Wang, Sha |
title |
Digital Watermarking Based Image and Video Quality Evaluation |
title_short |
Digital Watermarking Based Image and Video Quality Evaluation |
title_full |
Digital Watermarking Based Image and Video Quality Evaluation |
title_fullStr |
Digital Watermarking Based Image and Video Quality Evaluation |
title_full_unstemmed |
Digital Watermarking Based Image and Video Quality Evaluation |
title_sort |
digital watermarking based image and video quality evaluation |
publishDate |
2013 |
url |
http://hdl.handle.net/10393/23988 |
work_keys_str_mv |
AT wangsha digitalwatermarkingbasedimageandvideoqualityevaluation |
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1716579751644102656 |