360° video can provide users with immersive experience by showing the omnidirectional perspective, which is getting more attractive to consumers. However, 360° video tends to have higher resolution, resulting in increased bandwidth requirements for transmission. The characteristic of head-mounted displays (HMD) provides a new approach to reducing the cost of streaming 360° video bandwidth, which can encode 360° video by considering user’s orientation. In this paper, we propose a novel 360° video coding method based on the characteristics of Equi-rectangular Projection (ERP) and combined with user-oriented behavior. Specifically, a non-uniform tile method according is designed to the principle of ERP, which also meets the behavioral of users viewing 360° video. Additionally, appropriate coding parameters are set according to the positions of different tiles to reduce the redundancy introduced by oversampling to improve the coding efficiency. Experimental results show the proposed method can reduce the bandwidth requirement of streaming 360° video while ensuring the consistent visual quality, significantly.
In order to display a high dynamic range (HDR) image on a standard monitor, tone-mapping operators (TMOs) aim to compress HDR images into low dynamic range tone-mapped (TM) images. To accurately evaluate the performance of different TMOs, this paper proposes a no-reference image quality assessment (IQA) method for TM images. Firstly, the image is divided into dark area, middle area and bright area by using clustering algorithm. The entropy and area ratio features are extracted from three areas mentioned above and the saliency area that is detected by the proposed method. Then the natural scene statistics features of the luminance channel and RGB color channels of TMI are used to assess the luminance naturalness and chrominance naturalness, respectively. Finally the support vector regression module is utilized to yield a quality score of the TM images. The experimental results on the tone-mapped image database (TMID) show the effectiveness of the proposed algorithm. Compared with the existing representative IQA methods, the proposed method has better performance.
Light field has richer scene information than traditional images, including not only spatial information but also directional information. Aiming at multiple distortion problem of dense light field, combining with spatial and angular domain information, a light field image quality assessment method based on dense distortion curve analysis and scene information statistics is proposed in this paper. Firstly, the mean difference between all multi-view images in the angular domain of dense light field is extracted, and a corresponding distortion curve is drawn. Three statistical features are obtained by fitting the curve, which are slope, median and peak, respectively represent the distortion deviation, interpolation period and the maximum distortion. Then, the mean information entropy and mean gradient magnitude of the light field are extracted as the global and local features of the spatial domain. Finally, the extracted features are trained and tested by the Support Vector Regression. The experiment is conducted on the public MPI dense light field database. Experimental results show that the PLCC of the proposed method is 0.89, better than the existing methods, especially for different types of distorted contents.