Fusion of local degradation features for No-Reference Video Quality Assessment

Dimitrievski, Martin; Ivanovski, Zoran

doi:10.1109/mlsp.2012.6349737

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…It has been adopted for FR-IQA in [42] and for FR-VQA in [43]. A trained epsilon-SVR model was used in an NR-VQA algorithm to predict the video quality from the joint and marginal distributions of local wavelet coefficients [44].…”

Section: B Nonlinear Mappingmentioning

confidence: 99%

No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis

Zhu

Asari

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A discrete cosine transform (DCT)-based no-reference video quality prediction model is proposed that measures artifacts and analyzes the statistics of compressed natural videos. The model has two stages: 1) distortion measurement and 2) nonlinear mapping. In the first stage, an unsigned ac band, three frequency bands, and two orientation bands are generated from the DCT coefficients of each decoded frame in a video sequence. Six efficient frame-level features are then extracted to quantify the distortion of natural scenes. In the second stage, each frame-level feature of all frames is transformed to a corresponding video-level feature via a temporal pooling, then a trained multilayer neural network takes all video-level features as inputs and outputs, a score as the predicted quality of the video sequence. The proposed method was tested on videos with various compression types, content, and resolution in four databases. We compared our model with a linear model, a support-vector-regression-based model, a state-of-the-art training-based model, and a four popular full-reference metrics. Detailed experimental results demonstrate that the results of the proposed method are highly correlated with the subjective assessments. IndexTerms-Blocking artifact, discrete cosine transform (DCT), H.264/Advanced Video Coding (AVC), natural scene, no-reference (NR) measure, video quality assessment (VQA).

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Section: B Nonlinear Mappingmentioning

confidence: 99%

No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis

Zhu

Asari

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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“…This process is expensive and strenuous, however, the results obtained from a mass testing of human subjects are invaluable. In he past several years, algorithms emerged that can mimic this human behavior with high degree of success [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Our previous efforts [6] to NR VQA focused almost entirely on the spatial spread of energy and managed to estimate the subjective scores only marginally. The key problem is the lack of theoretical knowledge about how people perceive the various spatio-temporal artifacts, which impact the quality of experience (QoE).…”

Section: Introductionmentioning

confidence: 99%

No-reference quality assessment of highly compressed video sequences

Dimitrievski¹,

Ivanovski²

2013

2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP)

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In this paper we present a no-reference quality assessment algorithm for highly compressed H.264 videos. By analyzing the spatio-temporal artifacts and their effect on perceived visual quality we produced an array of viable predictors of quality. Using a feature selection method a small sub-set of features, each applied to a specific artefact-domain, was selected for optimal quality estimation. The features are mapped into video quality scores using a simple linear function, which is computationally efficient and minimizes the effect learning of content. The resulting algorithm is evaluated on content independent sets of highly compressed H.264 video sequences from the LIVE video database where it shows high correlation with the subjective scores.

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Fusion of local degradation features for No-Reference Video Quality Assessment

Cited by 2 publications

References 16 publications

No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis

No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis

No-reference quality assessment of highly compressed video sequences

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