A Novel No-reference Subjective Quality Metric for Free Viewpoint Video Using Human Eye Movement

Abstract

The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we therefore devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants' eye-tracker recorded spatio-temporal gazedata indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.

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“…The MOS which is a well-established quality measurement tool in telecommunication engineering has been used for evaluating the quality of experience of the users [29]- [31]. In our experiment, we have also used the MOS to evaluate the user experience in accessing the proposed device.…”

Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

“…The subjective studies could yield valuable data to evaluate the performance of objective methods towards aiming the goal of matching human perception. The subjective estimation employs the utilization of human visual attention and its parameters such as pupil size, pupil dilation, timestamp data, central tendency, fixation pattern, and gaze plot [9]- [12]. The objective estimation, for example, Graph based visual saliency (GBVS), on the other hand, focuses on colour contrast, brightness and motion on spatiotemporal domain [13].…”