No-reference stereo image quality assessment based on joint wavelet decomposition and statistical models

Abstract

The widespread use of 3D acquisition and display technologies has increased the interest of stereo image dataset in various application fields. As a result, it becomes necessary to have an efficient 3D quality assessment method to measure the human perception of stereoscopic images. While most of the state-of-the-art methods belong to the class of full-reference methods which require the original stereo images to be able to assess the quality, we propose in this paper a no-reference quality metric which does not require any information of the original stereo images. The proposed method operates in the wavelet transform domain and adopts a statistical framework to predict the quality of stereo images. More precisely, a joint wavelet decomposition is first performed on the stereo images to exploit simultaneously the intra and inter-views redundancies. A wavelet transform is also applied to their associated estimated disparity maps. Then, relevant features are extracted from the resulting wavelet subbands by resorting to appropriate statistical models. Simulations, carried out on the standard Live 3D image quality database, show that our proposed design model achieves significant improvement compared to the state-of-the-art 3D quality assessment methods.