Objective No-Reference Stereoscopic Image Quality Prediction Based on 2D Image Features and Relative Disparity

Stereoscopic images are widely used to enhance the viewing experience of three-dimensional (3D) imaging and communication system. In this paper, we propose an image feature and disparity dependent quality evaluation metric, which incorporates human visible system characteristics. We believe perceived distortions and disparity of any stereoscopic image are strongly dependent on local features, such as edge (i.e., nonplane areas of an image) and nonedge (i.e., plane areas of an image) areas within the image. Therefore, a no-reference perceptual quality assessment method is developed for JPEG coded stereoscopic images based on segmented local features of distortions and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness and the edge distortion within the block of images are evaluated in this method. Subjective stereo image database is used for evaluation of the metric. The subjective experiment results indicate that our metric has sufficient prediction performance

Impact of subjective dataset on the performance of image quality metrics

The IRCCyN/IVC subjective database [1] consists in 10 original colour images with a resolution of 512×512 pixels from which 235 distorted images have been generated, using 4 difhal-00321663

Computational Model for Stereoscopic Image Quality Prediction

In the modern era of Internet along with 3D imaging and communication system, many user-end applications require the estimation of quality of 3D images directly from the bit streams, as the original image may not be available. Though several metrics have been proposed in literature to assess the full reference perceptual quality of 3D images, however no reference quality assessment is still undeveloped which is a challenging issue. Therefore, in this paper, we propose a no reference stereoscopic image quality evaluation model based on image artifacts and disparity measure with the incorporation of Human visual system (HVS) characteristics. Based on HVS, we believe that perceptual artifacts of any image are strongly dependent on local features, such as plane and non-plane areas. For this reason, plane and non-plane area based blockiness and blur artifacts and also disparity are measured in this model. The experimental results show that the proposed model gives high correlation with subjective Mean Opinion Score (MOS)

No-Reference Perceptual Blockiness Estimation Method for JPEG Coded Images

Abstract: In this paper, we propose an efficient no-reference perceptual blockiness estimation method based on local features and segmentation for JPEG coded images that can automatically quantify perceptual blocking artifacts. We believe that perceptual blockiness of any image is strongly dependent on local features such as edge, flat and texture. Therefore, edge, flat, and texture based blockiness are evaluated in this method. Experimental results on LIVE database show that proposed method has sufficient correlation with subjective evaluation scores