Picture Quality Assessment System by Three-Layered Bottom-up Noise Weighting considering Human Visual Perception

Hamada, Takahiro; Miyaji, Satoshi; Matsumoto, Shuichi

doi:10.5594/j04354

Cited by 23 publications

(8 citation statements)

References 1 publication

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“…Simplicity is an important goal, since one would like the metric to run in real time and require only modest computational resources. One of the most complex and time-consuming elements of other proposed metrics 7,11,12,15,16 is the spatial filtering operation employed to implement the multiple, bandpass spatial filters that are characteristic of human vision. We accelerate this step by using the discrete cosine transform for this decomposition into spatial channels.…”

Section: Dvqmentioning

confidence: 99%

“…[7][8][9][10][11][12] Possible disadvantages of these metrics are that they may either not be based closely enough upon human perception, in which case they may not accurately measure visual quality, or that they may require amounts of memory or computation that restrict the contexts in which they may be applied. The goal of this project has been to construct a metric that is reasonably accurate but computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

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Digital video quality metric based on human vision

Watson

McGowan

2001

J. Electron. Imaging

303

156

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The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (digital video quality) [A. B. Watson, in Human Vision, Visual Processing, and Digital Display VIII, Proc. SPIE 3299, 139-147 (1998)]. Here, we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the discrete cosine transform. It incorporates aspects of early visual processing, including light adaptation, luminance, and chromatic channels; spatial and temporal filtering; spatial frequency channels; contrast masking; and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers.

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Section: Dvqmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital video quality metric based on human vision

Watson

McGowan

2001

J. Electron. Imaging

303

156

View full text Add to dashboard Cite

show abstract

“…This method can obtain a good objective-to-subjective correlation, while needs expensive cost. VQEG has executed a test program to compare subjective evaluations to some famous objective measurement methods [2,3,4], and found no one is able to replace the subjective testing [5].…”

Section: Introductionmentioning

confidence: 99%

Objective Perceptual Video Quality Measurement using a Foveation-Based Reduced Reference Algorithm

Fang

Jiang

Sun

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

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The properties and limitations of human visual system (HVS) are usually incorporated into objective video quality techniques for better correlation with subjective methods. In this paper, we propose a new objective assessment algorithm based on reduced reference video quality metrics (RR-VQM). The key point of our method is to design multilayer Spatial-Temporal (ST) regions based on human's fovea region, which can utilize the nonuniform resolution properties of HVS for high definition (HD) videos. Quality features are then extracted from ST regions for computing the video quality. Experimental results shows that this kind of multi-layer ST regions can improve the performance of objective methods, not only the results of evaluation, but can also accelerate the evaluation speed and reduce the amount of quality parameters to a large extent.

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“…One is a method based on comparison with the original picture, and the other is a method based only on the received picture. Various methods of comparing the received picture and the original picture have already been proposed [4]. In order to realize objective quality evaluation on the receiving side during transmission, however, the picture must be evaluated without reference to the original picture.…”

Section: (4) Multiple Description Codingmentioning

confidence: 99%

A flat, multiscalable, highly efficient and reliable video transmission scheme

Kawada

Matsumoto

2003

Electron. Comm. Jpn. Pt. I

Self Cite

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SUMMARYThe transmission of video materials by digital compression has been widely applied in recent years. The following problems are involved in this approach. (1) A duplex system composed of main and backup links is often used in the transmission of video materials. Thus, the transmission capacity of the backup system becomes redundant in the normal state. (2) No automatic image quality monitoring method for video transmission has been established. On the other hand, the coding/decoding techniques for digital video transmission are almost fully established, and various devices are already widely used. Thus, it is highly desirable to reduce the above redundancy, that is, to achieve highly efficient automatic picture quality monitoring. Consequently, this paper proposes a flat multiscalable video transmission scheme in which highly efficient and highly reliable video transmission with automatic picture quality monitoring can be realized by simply adding preand postprocessing, without modifying the existing coding/decoding devices. In the proposed scheme, noise cancellation by averaging and high picture quality are realized by processing that reduces the correlation of coding noise in the decoded video pictures of the two systems. The SN ratios of the received video and the original pictures can be estimated from the residual correlation. The proposed scheme can be applied not only to improving the efficiency of video materials transmission, but also to improving the reliability of picture transmission over the Internet.

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Picture Quality Assessment System by Three-Layered Bottom-up Noise Weighting considering Human Visual Perception

Cited by 23 publications

References 1 publication

Digital video quality metric based on human vision

Digital video quality metric based on human vision

Objective Perceptual Video Quality Measurement using a Foveation-Based Reduced Reference Algorithm

A flat, multiscalable, highly efficient and reliable video transmission scheme

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