No-Reference Stereoimage Quality Assessment for Multimedia Analysis Towards Internet-of-Things

Abstract

With continuous progress of Internet of Things, multimedia analysis in it has attracted more and more attention. Specially, stereoscopic display technology plays an important role in the multimedia analysis processing. In the Internet of Things system, the quality of stereoscopic image will be reduced in the transmission process. In this mode, it will have a great impact on multimedia analysis to judge whether the quality of stereoscopic image meets the requirements. In this paper, a new no-reference stereoscopic image quality assessment model for multimedia analysis towards Internet of Things is built, which is based on a deep learning model to learn from the class labels and image representations. In our framework, images are represented by natural scene statistics features that are extracted from discrete cosine transform domain, and a regression model is employed to shine upon the quality from the feature vector. The training process of the proposed model contains an unsupervised pretraining phase and a supervised fine-tuning phase, enabling it to generalize over the whole distortion types and severity. The proposed model greatly shows the correlation with subjective assessment as demonstrated by experiments on the LIVE 3-D Image Quality Database and IVC 3-D Image Quality Database.