Spatial contrast sensitivity and discrimination in pictorial images

Abstract

This paper describes continuing research concerned with the measurement and modeling of human spatial contrast sensitivity and discrimination functions, using complex pictorial stimuli. The relevance of such functions in image quality modeling is also reviewed. Previously 1,2 we presented the choice of suitable contrast metrics, apparatus and laboratory setup , the stimuli acquisition and manipulation, the methodology employed in the subjective tests and initial findings. Here we present our experimental paradigm, the measurement and modeling of the following visual response functions: i) Isolated Contrast Sensitivity Function (iCSF); Contextual Contrast Sensitivity Function (cCSF); Isolated Visual Perception Function (iVPF); Contextual Visual Perception Function (cVPF). Results indicate that the measured cCSFs are lower in magnitude than the iCSFs and flatter in profile. Measured iVPFs, cVPFs and cCSFs are shown to have similar profiles. Barten's contrast detection model 3 was shown to successfully predict iCSF. For a given frequency band, the reduction, or masking of cCSF compared with iCSF sensitivity is predicted from the linear amplification model (LAM) 4. We also show that our extension of Barten's contrast discrimination model 1,5 is capable of describing iVPFs and cVPFs. We finally reflect on the possible implications of the measured and modeled profiles of cCSF and cVPF to image quality modeling.

Bridging the Gap Between Imaging Performance and Image Quality Measures

“…Decades of successful STV-IQM usage suggests this assumption may be valid to some extent, although HVS linearity only applies near to threshold limits [35]. The question whether image quality is a function of threshold or suprathreshold vision has been debated; it is likely that both processes are involved [36]. In either case, the analyzed image signals are complex, and viewed in the context of other image content, thus introducing contrast and texture masking effects.…”

“…In either case, the analyzed image signals are complex, and viewed in the context of other image content, thus introducing contrast and texture masking effects. We propose that Contextual Contrast Sensitivity Functions (cCSFs -threshold models), and Contextual Visual Perception Functions (cVPFs -suprathreshold models) [36] are more appropriate models than the CSFs currently employed, which are based upon viewing isolated simple stimuli. cCSF and cVPF describe detection and discrimination of real scene content respectively, and account for the visual masking that results from viewing complex image signals [36].…”

“…Both are expected to reduce STV-IQM inaccuracy due to imaging system scene-dependency (Factor 1, above). cCSF or cVPF [36] functions (or a combination of both) are substituted in place of the traditional CSF. Their inclusion should reduce STV-IQM inaccuracy due to lower-level HVS model limitations (Factor 2, above).…”

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

“…However, CSFs, whilst being very commonly used for the purpose, deal with contrast detection, thus the question on whether they are relevant to image quality modeling (which is concerned with suprathreshold visible contrasts) has been debated by many (1) (2) (3) (4) . Contrast detection (and contrast discrimination (1) (5) ) functions do not account for upper-level cortical processing, which we expect to be of relevance in image quality analysis.…”

“…The application of contrast sensitivity functions (CSFs), as weighting-functions (see Triantaphillidou et al 2014 for a review) (1) when integrating the imaging system's spatial characteristics, prioritizes contrast information according to HVS detection to contrast, and attenuates frequencies outside the spatial limits of the HVS.…”