Subjective image quality assessment based on objective image quality measurement factors

Park, Hyung-Ju; Har, Dong-Hwan

doi:10.1109/tce.2011.6018872

Cited by 24 publications

(9 citation statements)

References 7 publications

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“…A successful RR quality assessment method must achieve a good balance between the data rate of RR features and the accuracy of image quality prediction [22]. Although during the last few years more IQA methods have been offered, the PSNR, SSIM, and MSSIM are still widely used in various applications [24,26,32]. Here, some recently proposed IQA methods are reviewed.…”

Section: Full-reference (Fr)mentioning

confidence: 99%

Image quality assessment using edge based features

Attar

Shahbahrami

Rad

2015

Multimed Tools Appl

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There are many applications for Image Quality Assessment (IQA) in digital image processing. Many techniques have been proposed to measure the quality of an image such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Structural Similarity Index Measurement (MSSIM). In this paper, a new technique, namely, Edge Based Image Quality Assessments (EBIQA) is proposed. The proposed technique is based on different edge features which are extracted from original (distortion free) and distorted images. The new approach was implemented and tested using different images which have been taken from A57 and WIQ image databases. The experimental results show that the functionality of the EBIQA technique is better than the state of art IQA techniques. The proposed technique is consistent with the mean opinion score which makes it suitable for automatic image quality assessment. Â© 2015 Springer Science+Business Media New Yor

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Section: Full-reference (Fr)mentioning

confidence: 99%

Image quality assessment using edge based features

Attar

Shahbahrami

Rad

2015

Multimed Tools Appl

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“…Subjective tests are necessary in order to quantify the human perception of the quality of multimedia services; they are also useful because product improvements are based on the users' requirements [17].…”

Section: Introductionmentioning

confidence: 99%

Video Quality Metric for Streaming Service Using DASH Standard

Rodríguez

Rosa

Alfaia

et al. 2016

IEEE Trans. on Broadcast.

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Currently, dynamic adaptive streaming over HTTP (DASH) standard is used to change the video resolution according to the network capacity of the end user's. However, if the video resolution changes frequently, the attention of the users can be affected decreasing their quality-of-experience (QoE). In this context, this paper proposes a no-reference video quality metric that takes three impairment factors into account, initial buffering delay, temporal interruptions or pauses, and video resolution changes during a video transmission. Also, the temporal locations of pauses and resolution changes are considered. In order to perform this task, extensive subjective tests were conducted. Experimental results show that the evaluators' QoE is highly correlated with the frequency of pauses, video resolution changes and initial delay. Based on these results a video streaming quality metric for DASH services (VsQM DASH ) is formulated and implemented on PC and mobile hand-held devices. Finally, the performance of VsQM DASH is validated with additional subjective tests reaching a person correlation coefficient of 0.99. Furthermore, experimental results demonstrated that the proposed metric has low complexity because it is based on application level parameters and its energy consumption is negligible in relation to the energy consumed by the video player; therefore, can be easily implemented in consumer electronic devices.

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“…Objective image quality assessment is designed with the aim of interpreting the quality of degraded image automatically and responding consistently with the behavior of the HVS [1] [2]. A huge number of IQA algorithms have been emerged with the evolution of image processing technology, which can be divided into two categories, namely non-HVS based metric and HVS based metric.…”

Section: Introductionmentioning

confidence: 99%

On combining visual perception and color structure based image quality assessment

Ren

2016

Neurocomputing

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Subjective image quality assessment based on objective image quality measurement factors

Cited by 24 publications

References 7 publications

Image quality assessment using edge based features

Image quality assessment using edge based features

Video Quality Metric for Streaming Service Using DASH Standard

On combining visual perception and color structure based image quality assessment

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