The stereoscopic 3D industry has fallen short of achieving acceptable Quality of Experience (QoE) because of various
technical limitations, such as excessive disparity, accommodation-convergence mismatch. This study investigates the
effect of scene content, camera baseline, screen size and viewing location on stereoscopic QoE in a holistic approach.
240 typical test configurations are taken into account, in which a wide range of disparity constructed from the shooting
conditions (scene content, camera baseline, sensor resolution/screen size) was selected from datasets, making the
constructed disparities locate in different ranges of maximal disparity supported by viewing environment (viewing
location). Second, an extensive subjective test is conducted using a single stimulus methodology, in which 15 samples at
each viewing location were obtained. Finally, a statistical analysis is performed and the results reveal that scene content,
camera baseline, as well as the interactions between screen size, scene content and camera baseline, have significant
impact on QoE in stereoscopic images, while other factors, especially viewing location involved, have almost no
significant impact. The generated Mean Opinion Scores (MOS) and the statistical results can be used to design
stereoscopic quality metrics and validate their performance.
There exist limitations in the human visual system (HVS) which allow images and video to be reconstructed using fewer
bits for the same perceived image quality. In this paper we will review the basis of spatial masking at edges and show a
new method for generating a just-noticeable distortion (JND) threshold. This JND threshold is then used in a spatial
noise shaping algorithm using a compressive sensing technique to provide a perceptual coding approach for JPEG2000
coding of images. Results of subjective tests show that the new spatial noise shaping framework can provide significant
savings in bit-rate compared to the standard approach. The algorithm also allows much more precise control of distortion
than existing spatial domain techniques and is fully compliant with part 1 of the JPEG2000 standard.
This paper proposes an approach to improve the performance of peak signal-to-noise ratio (PSNR) and structural
similarity (SSIM) for image quality assessment in digital cinema applications. Based on the particularities of quality
assessment in a digital cinema setup, some attributes of the human visual system (HVS) are taken into consideration,
including the fovea acuity angle and contrast sensitivity, combined with viewing conditions in the cinema to select
appropriate image blocks for calculating the perceived quality by PSNR and SSIM. Furthermore, as the HVS is not able
to perceive all the distortions because of selective sensitivities to different contrasts, and masking always exists, we
adopt a modified PSNR by considering the contrast sensitivity function and masking effects. The experimental results
demonstrate that the proposed approach can evidently improve the performance of image quality metrics in digital
cinema applications.
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