A new low complex reference free video quality predictor

Rossholm, Andreas; Lovstroem, B.

doi:10.1109/mmsp.2008.4665177

Cited by 19 publications

(19 citation statements)

References 7 publications

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“…Our contribution in this paper builds on our previous works [5] and [6]. The use of neural network for image quality prediction has given results better than the use of linear regression technique [6].…”

Section: Introductionmentioning

confidence: 87%

“…The parameters used in these methods can be obtained without fully decoding a video. A low complexity solution of video quality prediction based on bitstreamlayer parameters is found in [5]. Low complexity has been achieved by using a simple multi-linear regression system for estimating a relationship between the parameters and quality values.…”

Section: Related Workmentioning

confidence: 99%

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A no-reference machine learning based video quality predictor

Shahid

Rossholm

Lövström

2013

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

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The growing need of quick and online estimation of video quality necessitates the study of new frontiers in the area of no-reference visual quality assessment. Bitstream-layer model based video quality predictors use certain visual quality relevant features from the encoded video bitstream to estimate the quality. Contemporary techniques vary in the number and nature of features employed and the use of prediction model. This paper proposes a prediction model with a concise set of bitstream based features and a machine learning based quality predictor. Several full reference quality metrics are predicted using the proposed model with reasonably good levels of accuracy, monotonicity and consistency.

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Section: Introductionmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

A no-reference machine learning based video quality predictor

Shahid

Rossholm

Lövström

2013

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

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“…Similarly, a low complexity solution of VQA based on bitstream features was proposed in Ref. 29. An improvement of this approach was included in Ref.…”

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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“…This makes the model comparatively more complex but it generally offers better performance. A lowcomplexity solution of video quality prediction based on bitstream extracted parameters is found in [153]. The features used are mainly related to the encoding parameters and are taken on sequence level.…”

Section: Bitstream Layer Modelmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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A new low complex reference free video quality predictor

Cited by 19 publications

References 7 publications

A no-reference machine learning based video quality predictor

A no-reference machine learning based video quality predictor

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

No-reference image and video quality assessment: a classification and review of recent approaches

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