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

Abstract

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.

Objective No-reference Stereo Video Quality Assessment Research

“…The result shows that the method is superior to other methods, and it is more practical. The experimental results are given in paper [8]. In this paper, these results illustrate the accuracy of current evaluation model.…”

“…The model parameters are based on the statistical characteristics, so the application range of the model was limited. In [8], a no reference stereo video evaluation model is proposed. The method is based on image segmentation, through calculating the extent of the blockiness, and then combines disparity estimation to the assessment of the quality of video.…”

Objective Evaluation Criteria for Stereo Camera Shooting Quality Under Different Shooting Parameters and Shooting Distances

“…13, are used to assess prediction monotonicity [45], [46]. For a perfect match between the objective and subjective scores, the following should keep valid: PLCC=SROCC=KROCC=1 and RMSE=0 [47]. Table XII presents the overall performance of the proposed quality evaluation criteria.…”

No‐Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation

“…Section 5 proposes our NOSPDM paradigm. In Section 6, experimental results using the Toyama database [3] are reported and analyzed. Eventually, conclusion is drawn and future work is discussed in Section 7.…”

“…First, the most general approaches are full-reference methods [1,2], assuming the reference image is fully known. In many practical applications, however, the reference image is not available, and the second type of methods, namely, noreference image quality metrics [3,4] is then desirable. The third type is referred to as reduced-reference IQA algorithm [5], which is applied to the situation where the reference image is only partially available, that is, some extracted features are made available as side information to help estimate quality of the distorted image.…”