In photography, the dynamic range (DR) is a distinguishable brightness range and is determined by the analog-to-digital converter (ADC) and signal-to-noise (SNR) performance of the sensor. Recently, many various HDR strategies have been introduced to obtain high DR beyond these hardware limitations. However, since camera manufacturers set these HDR algorithms to operate differently by considering the situation, it is necessary to evaluate the quality of images taken in various situations for objective evaluation. In order to quantitatively measure the DR, we should know both the actual luminous intensity and the SNR of the picture. However, it is difficult to measure the two information in general-scene photos without charts. To overcome these problems, in this study, we propose a method to measure the DR of a natural-scene photograph by reconstructing radiance map and specifying the pixel value at which the SNR reaches 12dB. Using the pre-calculated radiation and SNR information, we measured DR of photos without using a chart, and demonstrated that HDR images have higher DR than standard DR (SDR) images.
In this study, the brightness matching experiment was conducted to obtain the equivalent luminance between chromatic and achromatic colors. Observers adjusted the luminance of achromatic colors until achromatic colors were perceived as having the same brightness with chromatic colors. A total of 285 chromatic colors having three different luminance levels, 30cd/m^2, 95cd/m^2, and 300cd/m^2 were used as the test colors. Twenty observers participated in this experiment repeating three times. The results showed that the brightness-to-luminance (B/L) ratio, where brightness means the luminance of achromatic color, increases as CIE 1976 saturation increases in all luminance levels indicating the Helmholtz-Kohlrausch effect. Also, as the luminance level of chromatic color increases, B/L ratio decreases. It is found that the existing color appearance models predicting the H-K effect overestimate the brightness increment by chroma compared to our new heterochromatic brightness matching data set.
Unfortunately, images acquired by users from image acquiring devices like cameras could be under-exposed, overexposed, or backlit. These under-exposed, over-exposed, or backlit images are not suitable for recognizing the information contained in the images. In this paper, we propose a new technique which corrects the brightness of already acquired images from image acquiring devices. This technique divides the image area to determine the state of brightness and operates according to the results of the state analysis without any threshold or magic value. It effectively corrects the brightness of under-exposed, overexposed, or backlit images using only image data analysis.
Viewers of high dynamic range television (HDR, HDR-TV) expect a comfortable viewing experience with significantly brighter highlights and improved details of darker areas on a brighter display. However, extremely bright images on a HDR display are potentially undesirable and lead to an uncomfortable viewing experience. To avoid the issues, we require specific production guidelines for subjective brightness to ensure brightness consistency between and within programs. To create such production guidelines, it is necessary to develop an objective metric for subjective brightness in HDR-TVs. A previous study reports that the subjective brightness is proportional to the average of displayed pixel luminance levels. However, other parameters can affect the subjective brightness. Therefore, we conducted a subjective evaluation test by using specific test images to identify the factors that affect the perceived overall brightness of HDR images. Our results indicated that positions and distributions of displayed pixel luminance levels on video affect brightness in addition to the average of displayed pixel luminance levels. The study is expected to contribute to the development of an objective metric for subjective brightness.