A reduced complexity no-reference artificial neural network based video quality predictor

Shahid, Muhammad; Rossholm, Andreas; Lövström, Benny

doi:10.1109/cisp.2011.6099931

Cited by 23 publications

(23 citation statements)

References 10 publications

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“…Objective methods are categorized as full reference (FR), reduced reference (RR) and no reference (NR) methods based on the reference information required. Examples of each type include [7], [8], [9] for FR, RR and NR respectively. Most of the objective methods estimate visual quality by quantifying spatial (intra frame) degradations such as blocking, blurring, ringing and temporal (inter frame) degradations such as jitter [10].…”

Section: Introductionmentioning

confidence: 99%

Subjective quality assessment of H.264/AVC encoded low resolution videos

Shahid

Singam

Rossholm

et al. 2012

2012 5th International Congress on Image and Signal Processing

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Abstract-Advancements in the video processing area have been proliferated by services that require low delay. Such services involve applications being offered at various temporal and spatial resolutions. It necessitates to study the impacts of related video coding conditions upon perceptual quality. But most of studies concerned with quality assessment of videos affected by coding distortions lack in variety of spatio-temporal resolutions. This paper presents a work done on quality assessment of videos encoded by state-of-the-art H.264/AVC standard at different bitrates and frame rates. Overall, 120 test scenarios for video sequences having different spatial and temporal spectral information were studied. The used coded bistreams in this work and the corresponding subjective assessment scores have been made public for the research community to facilitate further studies.

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Section: Introductionmentioning

confidence: 99%

Subjective quality assessment of H.264/AVC encoded low resolution videos

Shahid

Singam

Rossholm

et al. 2012

2012 5th International Congress on Image and Signal Processing

Self Cite

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“…Learning tools have proven to be a promising solution for this type of approaches as well as for assessing QoE [21]. Shahid et al [22] proposed a model combining different bitstream-layer features using an Artificial Neural Network to estimate the quality. However, their method focused on a very particular case, not providing results with sufficient general validity.…”

Section: No-reference Oqoe Methods For Video Quality Assessmentmentioning

confidence: 99%

Cognitive no-reference video quality assessment for mobile streaming services

Vega

Giordano

Mocanu

et al. 2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.

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“…A further improvement of Ref. 30 can be found in Ref. 31, in which a larger set of parameters was used and the estimation of subjective MOS was also considered.…”

Section: Related Workmentioning

confidence: 99%

“…An improvement of this approach was included in Ref. 30, in which the required number of features was reduced so as to promote computational efficiency. In that work, an improvement was noted in estimation accuracy by the virtue of the usage of an artificial neural network.…”

Section: Related Workmentioning

confidence: 99%

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

Shahid

Pandremmenou

Kondi

et al. 2016

J. Electron. Imaging

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This is the published version of a paper published in Journal of Electronic Imaging (JEI).Citation for the original published paper (version of record):Shahid, M. (2016) Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models. Journal of Electronic Imaging (JEI)Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. Abstract. Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.

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A reduced complexity no-reference artificial neural network based video quality predictor

Cited by 23 publications

References 10 publications

Subjective quality assessment of H.264/AVC encoded low resolution videos

Subjective quality assessment of H.264/AVC encoded low resolution videos

Cognitive no-reference video quality assessment for mobile streaming services

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

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