Contrast sensitivity function and image discrimination

Abstract

A previous study tested the validity of simulations of the appearance of a natural image (from different observation distances) generated by using a visual model and contrast sensitivity functions of the individual observers [J. Opt. Soc. Am. A 13, 1131 (1996)]. Deleting image spatial-frequency components that should be undetectable made the simulations indistinguishable from the original images at distances larger than the simulated distance. The simulated observation distance accurately predicted the distance at which the simulated image could be discriminated from the original image. Owing to the 1/f characteristic of natural images' spatial spectra, the individual contrast sensitivity functions (CSF's) used in the simulations of the previous study were actually tested only over a narrow range of retinal spatial frequencies. To test the CSF's over a wide range of frequencies, the same simulations and testing procedure were applied to five contrast versions of the images (10-300%). This provides a stronger test of the model, of the simulations, and specifically of the CSF's used. The relevant CSF for a discrimination task was found to be obtained by using 1-octave Gabor stimuli measured in a contrast detection task. The relevant CSF data had to be measured over a range of observation distances, owing to limitations of the displays.

Portable LCD Image Quality: Effects of Surround Luminance

“…Since CSFs have been widely used for evaluating image quality by predicting the perceptible differences between a pair of images (Barten, 1990;Daly, 1993;Zhang & Wandell, 1996;Wang & Bovik, 1996) surround luminance effects on CSF can be very useful for this application. Furthermore, the results can also be applied to simulate the appearance of a scene (Peli, 1996(Peli, , 2001) and evaluate the visual performance of the eye. (Yoon & Williams, 2002) …”

A novel color image compression algorithm using the human visual contrast sensitivity characteristics

“…Therefore, the researchers have obtained different results by performing different experiments. By far, the multi-parameter composite model proposed by Dally et al is considered as the most suitable model to describe the luminance contrast sensitivity characteristics of the HVS, which is commonly known as Daly model [10]. It is mathematically expressed by (4), where f is the angular frequency (cycle/degree), L is the average luminance of every grating, 2 i describes the size of the image (assuming the image is square), and ε is the frequency scaling constants, whose value is 0.9.…”

“…The value of θ r is equal to 1, when the orientation angle (α ) of the strips is π/2. The tested targets are observed through the 2-mm artificial pupil by the monocular vision, and the centrifugal rate from the central recess is 1 mm [10][11]. …”

“…In recent years, a great deal of researches on image compression have been carried out, and many image coding technologies and standards have been developed [4][5][6][7]. These coding techniques mainly involve the image compression technology based on the human visual and auditory characteristics, wavelet transform theory, and fractal theory [8][9][10][11][12][13]. However, the contrast sensitivity characteristics of the human visual system (HVS) have not been exploited much in the field of color image compression [1][2][3].…”

Image Deconvolution Ringing Artifact Detection and Removal via PSF Frequency Analysis

“…Typically, contrast is estimated by Weber's formula: C l = (l o − l b )/l b where l o and l b are the luminance of the object and its surrounding background, respectively. More complex contrast analysis can be performed by employing discrete cosine transform and wavelets [30,31]. We use the local mean mean(l) and standard deviation std(l) of the image intensity to estimate the contrast of a region with its background [32] C l = std(l)/mean(l).…”