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Three-dimensional (3D) imaging has attracted considerable attention recently due to its increasingly wide range of applications. Consequently, perceived quality is a great important issue to assess the performance of all 3D imaging applications. Perceived distortion and depth of any stereoscopic images are strongly dependent on the local features, such as edge, flat and texture. In this paper, we propose an noreference (NR) perceptual quality assessment for IPEG coded stereoscopic images based on segmented local features of artifacts and disparity. The local features information of stereoscopic pair images such as edge, flat and texture areas and also the blockiness and zero crossing rate within the block of the images are evaluated for artifacts and disparity in this method. The result on our subjective stereoscopic images database indicates that the model performs quite well over a wide rang of image content and distortion levels.

The interest in objective quality assessment have significantly increased over the past decades. Several objective quality metrics have been proposed and made publicly available, moreover, several subjective quality assessment databases are distributed in order to evaluate and compare the metrics. However, several question arises: are the objective metrics behaviours constant across databases, contents and distortions? how significantly the subjective scores might fluctuate on different displays (i.e. CRT or LCD)? which objective quality metric might best evaluate a given distortion? In this article, we analyse the behaviour of four objective quality metrics (including PSNR) tested on three image databases. We demonstrate that the performances of the quality metrics can strongly fluctuate depending on the database used for testing. We also show the consistency of all metrics for two distinct displays.

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.

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