Video Quality Assessment

Seshadrinathan, Kalpana; Bovik, Alan C.

doi:10.1016/b978-0-12-374456-2.00013-x

Cited by 17 publications

(18 citation statements)

References 35 publications

(33 reference statements)

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“…The range of values of the Temporal MOVIE scores is smaller than that of the spatial scores, due to the large divisive normalization in (19) and (20). To offset this effect, we use the square root of the temporal scores.…”

Section: E Pooling Strategymentioning

confidence: 99%

“…The denominator terms in (19) and (20) ensure that temporal quality measurement is relatively insensitive to spatial distortions, thus avoiding redundancy in the spatial and temporal quality measurements. For example, in the case of blur, we would expect that the same Gabor filters are activated by the reference and distorted videos.…”

Section: Temporal Movie Indexmentioning

confidence: 99%

“…The vector ν d represents the response of the distorted video to a mechanism that is tuned to the motion of the reference video. Thus, any deviation between the reference and distorted video motions are captured by (19) and (20).…”

Section: Temporal Movie Indexmentioning

confidence: 99%

“…The constant C 3 is added to prevent numerical instability when the denominators of (19) or (20) go to 0. This can happen in smooth image regions.…”

Section: Temporal Movie Indexmentioning

confidence: 99%

“…It is well known that the MSE does not correlate well with visual quality, which is the reason why research into full reference VQA techniques has been intensely studied [18]; see [19] for a review of VQA. Several types of weighted MSE and Peak Signal to Noise Ratio (PSNR) have also been proposed by researchers; see, for example, [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

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Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

Seshadrinathan

Bovik

2010

IEEE Trans. on Image Process.

647

402

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Abstract-There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.

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Section: E Pooling Strategymentioning

confidence: 99%

Section: Temporal Movie Indexmentioning

confidence: 99%

Section: Temporal Movie Indexmentioning

confidence: 99%

“…The constant C 3 is added to prevent numerical instability when the denominators of (19) or (20) go to 0. This can happen in smooth image regions.…”

Section: Temporal Movie Indexmentioning

confidence: 99%