No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion - Vishwakarma, Anish Kumar; Bhurchandi, Kishor M. (Journal of visual communication and image representation, 2022)
This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa. •3D steerable wavelet of local 3D video blocks’ NVS spatiotemporal features.•Noise is modeled using simple arithmetic on sampled spatiotemporal video planes.•RGB histogram, color gamut and entropy based novel human perceptual features.•A novel two-stage hybrid regression scheme to achieve score level fusion.•A novel scheme is effective for both synthetic and authentic distortions.