Review Article

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Figure 4

Block diagram of a typical computational neural model of V1 used to predict masked detection thresholds (here with a noise mask and a circular sine-wave target). Three key stages are employed in most neural models: (1) a frequency-based decomposition which models the initially linear responses of an array of visual neurons, (2) application of a pointwise nonlinearity to the decomposition coefficients and inhibition based on the values of other coefficients [99, 102ā€“104], and (3) pointwise differences between the adjusted coefficients and summation of these adjusted coefficient differences across space, spatial frequency, and orientation so as to arrive at a single scalar differential response value or a map of differential response values.
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