Z. M. Parvez Sazzad

No-reference stereoscopic image quality assessment

Display of stereo images is widely used to enhance the viewing experience of three-dimensional imaging and communication systems. In this paper, we propose a method for estimating the quality of stereoscopic images using segmented image features and disparity. This method is inspired by the human visual system. We believe the perceived distortion and disparity of any stereoscopic display is strongly dependent on local features, such as edge (non-plane) and non-edge (plane) areas. Therefore, a no-reference perceptual quality assessment is developed for JPEG coded stereoscopic images based on segmented local features of artifacts and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness and the blur within the block of images are evaluated in this method. Two subjective stereo image databases are used to evaluate the performance of our method. The subjective experiments results indicate our model has sufficient prediction performance.

Stereoscopic image quality prediction

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 JPEG 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.

Spatio-temporal segmentation based continuous no-reference stereoscopic video quality prediction

In this paper, we propose a no-reference continuous video quality prediction method for MPEG-2 MP@ML coded stereoscopic videos based on spatio-temporal segmentation. Segmented local features such as edge and non-edge areas based spatial artifacts, disparity, and temporal features are measures in this method. Blockiness and blur are considered to measure spatial artifacts for each stereo pair frames. A block based different zero-crossing approach is used for disparity measure. Each temporal segment is evaluated for spatial artifacts and disparity. In this method, temporal features are estimated separately for left and right video sequences based on segmented local features and sub temporal segment. Different weighting factors are then applied for the two different local features to measure the artifacts, disparity, and temporal features of a temporal segment. In order to verify the performance, we conducted subjective experiment on different symmetric and asymmetric coded stereo videos which indicates that our proposed methods prediction performance is quite sufficient.

Continuous stereoscopic video quality evaluation

This research aims to develop an objective no-reference video quality evaluation method for MPEG-2 MP@ML coded (symmetric and asymmetric) stereoscopic videos. Our proposed method is based on segmented local features of spatial artifacts, disparity, and temporal activities of videos. Segmented local features information such as edge and non-edge areas of any stereoscopic pair frames (i.e., left and right views) have taken into consideration for blockiness and zero crossing. In this method, a temporal segmentation approach is considered and each temporal segment is evaluated for artifacts and disparity. Temporal features are calculated separately for left and right video sequences based on segmented local features and sub temporal segment. Different weighting factors are also applied to measure the spatial artifacts, disparity, and temporal features of the segment. In order to verify the performance, we conducted subjective experiment on different symmetric and asymmetric coded (Bit rates: 2, 3, 5, and 8 Mbps) stereo video pairs. An auto stereoscopic display was used for fifteen (15) reference stereo videos; each of the video was 15 seconds length and the total length of each test sequence was (15×15 sec = 3 min 45 sec). Seven video sequences were used to complete the experiment. The Single Stimulus Continuous Quality Evaluation (SSCQE) method was used to conduct our subjective experiment. The experiment result indicates that our proposed method has given sufficient prediction performance.

Image Quality Evaluation Model Based on Local Features and Segmentation

The perceived image distortion of any image is strongly depend on the local features, such as edge, flat and texture. A new objective no-reference (NR) image quality evaluation model based on local features and segmentation for JPEG coded image is presented in our previous paper, which is easy to calculate and applicable to various image processing applications. But the algorithmic thresholds investigation of the segmentation algorithm were not sufficient in the paper. Therefore in this paper, we want to investigate the suitable threshold values of our segmentation algorithm both for training and test images by the optimization method. Our experiments on various image distortion types indicate that its performs significantly better than the conventional model.

Local region-based image quality assessment independent of JPEG and JPEG2000 coded color images

The importance of the perceived quality measurement is fundamental for many image processing applications, such as compression, acquisition, restoration, enhancement, and reproduction. Color information is also of great importance for the perceived image quality, although perceived information is mainly represented by luminance. We present a computational and memory-efficient no-reference image quality assessment model independent of JPEG and JPEG2000 coded color images based on local regions. We also present the discrimination algorithm for these two types of coded images. The features of local regions are blockiness around the block boundary, average absolute difference between adjacent pixels within the block, and zero crossing rate within the block of the image. We validate the performance of our model on our subjective database, which shows good quality prediction performance, and the models generalization ability is also verified on the other database.