In mixed-resolution (MR) stereoscopic video, one of the views has a lower resolution compared to the other one, hence providing means for improved compression. The underlying assumption in MR stereoscopic video is that the human visual system fuses the left and right views in such a way that the perceived image quality is close to that of the higher-resolution view. This paper describes a subjective quality evaluation experiment with uncompressed MR stereoscopic video, in which the aim was to discover the approximate limit of the downsampling ratio where the perceived quality is still close to the quality of the higher view. Different downsampling ratios, namely 1/2, 3/8, and 1/4 along both coordinate axes, were tested. The threshold of the higher-resolution view being dominant in the perceived quality lied in between downsampling ratios 1/2 and 3/8, corresponding to 11.4 and 7.6 pixels per degree of viewing angle, respectively. At downsampling ratios 3/8 and 1/4, the perceived quality linearly correlated with the average luma peak signal-to-noise ratio of the lower-resolution view.Index Terms -Mixed-resolution stereoscopic video, asymmetric stereoscopic video, binocular suppression

Asymmetric stereoscopic video coding takes advantage of the binocular suppression of the human vision by representing one of the views with a lower quality. This paper describes a subjective quality test with asymmetric stereoscopic video. Different options for achieving compressed mixed-quality and mixed-resolution asymmetric stereo video were studied and compared to symmetric stereo video. The bitstreams for different coding arrangements were simulcast-coded according to the Advanced Video Coding (H.264/AVC) standard. The results showed that in most cases, resolution-asymmetric stereo video with the downsampling ratio of 1/2 along both coordinate axes provided similar quality as symmetric and qualityasymmetric full-resolution stereo video. These results were achieved under same bitrate constrain while the processing complexity decreased considerably. Moreover, in all test cases, the symmetric and mixed-quality full-resolution stereoscopic video bitstreams resulted in a similar quality at the same bitrates.

Subjective image quality data for 9 image processing pipes and 8 image contents (taken with mobile phone camera, 72 natural scene test images altogether) from 14 test subjects were collected. A triplet comparison setup and a hybrid qualitative/quantitative methodology 1 were applied. MOS data and spontaneous, subjective image quality attributes to each test image were recorded. The use of positive and negative image quality attributes by the experimental subjects suggested a significant difference between the subjective spaces of low and high image quality. The robustness of the attribute data was shown by correlating DMOS data of the test images against their corresponding, average subjective attribute vector length data. The findings demonstrate the information value of spontaneous, subjective image quality attributes in evaluating image quality at variable quality levels. We discuss the implications of these findings for the development of sensitive performance measures and methods in profiling image processing systems and their components, especially at high image quality levels.

This study presents a methodology of forming contextually valid scales for subjective video quality measurement. Any single value of quality e.g. Mean Opinion Score (MOS) can have multiple underlying causes. Hence this kind of a quality measure is not enough for example, in describing the performance of a video capturing device. By applying Interpretation Based Quality (IBQ) method as a qualitative/quantitative approach we have collected attributes familiar to the end user and that are extracted directly from the material offered by the observers' comments. Based on these findings we formed contextually valid assessment scales from the typically used quality attributes. A large set of data was collected from 138 observers to generate the video quality vocabulary. Video material was shot by three types of video cameras: Digital video cameras (4), digital still cameras (9) and mobile phone cameras (9). From the quality vocabulary, we formed 8 unipolar 11-point scales to get better insight of video quality. Viewing conditions were adjusted to meet the ITU-T Rec. P.910 requirements. It is suggested that the applied qualitative/quantitative approach is especially efficient for finding image quality differences in video material where the quality variations are multidimensional in nature and especially when image quality is rather high.

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