No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning |
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Affiliation: | 1. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China;2. Anhui Longquan Silicon Materials Co.,Ltd, Huaiyuan, Anhui 233400, China;1. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China;2. College of Electronic and Information Engineering, Hebei University, Baoding 071002, China;3. College of Physics Science and Technology, Hebei University, Baoding 071002, China |
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Abstract: | As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model. |
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Keywords: | Stereoscopic image quality assessment Quaternion wavelet transform Feature extraction Heterogeneous ensemble learning Rotated feature space |
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