We are developing an automatic `image quality meter' for assessing the degree of impairment of broadcast TV images. The meter incorporates a model of the human visual system derived from psychophysical and neurophysiological studies. Early visual processing is assumed to consist of a set of spatially parallel, largely independent functional modules; but later stages are more heavily resource limited and constrained by limitations on attention and memory capacity. In line with CCIR recommendations, image evaluation can focus either on detection of the impairment itself (typically, superimposed lines or noise, or color dropout) or on assessment of the perceptible quality of the depicted scene. The observer may choose to attend to either aspect. Experimental studies of human subjects suggest that these two processes are largely independent of each other and subject to voluntary control. The meter captures images directly from TV via a CCD camera and digital sampling hardware. Early visual processes are emulated in software as a bank of spatial and temporal filters and higher level processes by a 3-layer neural network. Preliminary trials of the meter verify that it can produce quantitative CCIR gradings that match those made by an `expert' human assessor and it does so better than other electronic systems that do not incorporate the model of early human vision.
Ian R. L. Davies,
"Automated image quality assessment", Proc. SPIE 1913, Human Vision, Visual Processing, and Digital Display IV, (8 September 1993); doi: 10.1117/12.152711; https://doi.org/10.1117/12.152711