Video Quality Assessment Using Spatio-Velocity Contrast Sensitivity Function

Abstract

SUMMARYDue to the development and popularization of highdefinition televisions, digital video cameras, Blu-ray discs, digital broadcasting, IP television and so on, it plays an important role to identify and quantify video quality degradations. In this paper, we propose SV-CIELAB which is an objective video quality assessment (VQA) method using a spatio-velocity contrast sensitivity function (SV-CSF). In SV-CIELAB, motion information in videos is effectively utilized for filtering unnecessary information in the spatial frequency domain. As the filter to apply videos, we used the SV-CSF. It is a modulation transfer function of the human visual system, and consists of the relationship among contrast sensitivities, spatial frequencies and velocities of perceived stimuli. In the filtering process, the SV-CSF cannot be directly applied in the spatial frequency domain because spatial coordinate information is required when using velocity information. For filtering by the SV-CSF, we obtain video frames separated in spatial frequency domain. By using velocity information, the separated frames with limited spatial frequencies are weighted by contrast sensitivities in the SV-CSF model. In SV-CIELAB, the criteria are obtained by calculating image differences between filtered original and distorted videos. For the validation of SV-CIELAB, subjective evaluation experiments were conducted. The subjective experimental results were compared with SV-CIELAB and the conventional VQA methods such as CIELAB color difference, Spatial-CIELAB, signal to noise ratio and so on. From the experimental results, it was shown that SV-CIELAB is a more efficient VQA method than the conventional methods. key words: objective video quality assessment, spatio-velocity contrast sensitivity function, image diffrence, subjective evaluation, rank order correlation coefficient

Revisiting the Perceived Motion Model that Predicted 360 FPS Displays by S Daly 2021

“…It begins with a spatio-velocity model based on psychophysics [5] and modified for light adaptation to higher average luminance levels (100 cd/m^2). It was later tested in a psychophysical experiment for four different conditions of motion tracking [6] and incorporated into a color video quality metric [7]. The key concept was to measure and model moving Gabor patterns 1 of differing frequencies and characterize them in terms of velocity (temporal frequency could easily be calculated from the spatial frequency and velocity).…”