Reduced reference watermark-based image transmission quality metric

Kayvanrad, Mohammad H.; Sodagari, Shabnam; Avanaki, Alireza Nasiri; Ahmadi‐Noubari, H.

doi:10.1109/isccsp.2008.4537282

Cited by 3 publications

(5 citation statements)

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“…Another parameter used in [129] exploits video structural similarity index (VSSI) [130] for VQA. The results of VSSI have a good correlation with the subjective measure of MOS [80]. High-bandwidth channel video quality system of RR is shown in Fig.…”

Section: Low-bandwidth-based Rr Methodsmentioning

confidence: 65%

“…During transmission, the low-frequency feature contents of the multimedia and histogram features suffered distortion. These distortions are calculated by transmitting the low-level frequency features of original multimedia contents with their analogous histogram features at the receiver side by means of a strong watermark [80].…”

Section: Discrete Wavelet Transform Coefficient-based Methodsmentioning

confidence: 99%

“…The method proposed in [80] uses a low-frequency coefficient and low histogram information of the original image as a RR feature for estimating the channel-induced errors. This information is embedded in the original image as a watermark.…”

Section: Low-bandwidth-based Rr Methodsmentioning

confidence: 99%

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Reduced reference image and video quality assessments: review of methods

Dost

Saud

Shabbir

et al. 2022

J Image Video Proc.

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With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications.

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Section: Low-bandwidth-based Rr Methodsmentioning

confidence: 65%

Section: Discrete Wavelet Transform Coefficient-based Methodsmentioning

confidence: 99%

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Reduced reference image and video quality assessments: review of methods

Dost

Saud

Shabbir

et al. 2022

J Image Video Proc.

View full text Add to dashboard Cite

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“…We then embed the obtained level 1 approximation (LL1) coefficients into level 1 horizontal detail (LH1) and vertical detail (HL1) coefficients through a robust watermarking algorithm which is a modified version of [11].…”

Section: The Proposed Image Quality Assessment Metricmentioning

confidence: 99%

“…In this paper, we introduce a RR image quality assessment metric by choosing the approximation coefficients of a parameterized discrete wavelet transform (DWT), as a feature of the image at the transmitter [11], (as it contains the low frequency part to which the human visual system is more sensitive than higher frequencies), and embedding it by means of a robust (to various noise types) wavelet-based watermarking scheme within the original image. In watermarking, we optimize a parameterized DWT [12] so that the best wavelet transform for each image can be derived by solving a genetic algorithm optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Reduced reference image quality assessment metric using optimized parameterized wavelet watermarking

Avanaki

Sodagari

Diyanat

2008

2008 9th International Conference on Signal Processing

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We propose a novel reduced reference quality assessment metric for image transmission rooted in an optimization approach toward parameterized waveletbased data hiding. The approximation coefficients of one level parameterized wavelet transform of the original image at the transmitter are embedded into that of the horizontal and vertical detail coefficients in a robust and invisible manner to be used as a feature of the original image for comparisons at the receiver side. The best wavelet type used for decomposition is obtained by a curve fitting process, resulting in the most optimum parameters in terms of which the wavelet transform is formulated using genetic algorithm. Simulation results demonstrate the efficiency of our proposed image quality metric in that it has strong correlation with already established image quality metrics, e.g., Structural Similarity Index (SSIM). IndexTerms-Image communication, parameterized wavelet transform, reduced reference quality assessment, watermarking

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