Just noticeable differences in perceived image contrast with changes in displayed image size

An evaluation of the change in perceived image contrast with changes in displayed image size was carried out. This was achieved using data from four psychophysical investigations, which employed techniques to match the perceived contrast of displayed images of five different sizes. A total of twenty-four S-shape polynomial functions were created and applied to every original test image to produce images with different contrast levels. The objective contrast related to each function was evaluated from the gradient of the mid-section of the curve (gamma). The manipulation technique took into account published gamma differences that produced a just-noticeable-difference (JND) in perceived contrast. The filters were designed to achieve approximately half a JND, whilst keeping the mean image luminance unaltered. The processed images were then used as test series in a contrast matching experiment. Sixty-four natural scenes, with varying scene content acquired under various illumination conditions, were selected from a larger set captured for the purpose. Results showed that the degree of change in contrast between images of different sizes varied with scene content but was not as important as equivalent perceived changes in sharpness

Testing HDR image rendering algorithms

Eight high-dynamic-range image rendering algorithms were tested using ten high-dynamic-range pictorial images. A large-scale paired comparison psychophysical experiment was developed containing two sections, comparing the overall rendering performances and grayscale tone mapping performance respectively. An interval scale of preference was created to evaluate the rendering results. The results showed the consistency of tone-mapping performance with the overall rendering results, and illustrated that Durand and Dorsey’s bilateral fast filtering technique and Reinhard’s photographic tone reproduction have the best rendering performance overall. The goal of this experiment was to establish a sound testing and evaluation methodology based on psychophysical experiment results for future research on accuracy of rendering algorithms

Color reproduction using “Black-Point Adaptation”

Based on the current state of CIECAM97s, there is a missing adjustment associated with a black-point unlike a white-point. As an attempt to improve the performance of CIECAM97s for color reproduction, six algorithms focusing on “black-point adaptation” were generated based on previous work on white-point adaptation methods and gamut mapping methods. The six algorithms were used to reproduce four original images targeted to four simulated hard-copy viewing environments that were only differentiated by their black-point settings. Then, the six algorithms were tested in a psychophysical experiment with 32 observers. As a result, linear lightness rescaling under the luminances of white and black of a specific setting was demonstrated to be the best color reproduction method across different black-point settings. The adapted black-point was defined as having the lowest lightness value with its default chromatic appearance correlates predicted by the current state of CIECAM97s under the input viewing environment and was reproduced accordingly with the same appearance correlates

Digital Camera Workflow for High Dynamic Range Images Using a Model of Retinal Processing

We propose a complete digital camera workflow to capture and render high dynamic range (HDR) static scenes, from RAW sensor data to an output- referred encoded image. In traditional digital camera processing, demosaicing is one of the first operations done after scene analysis. It is followed by rendering operations, such as color correction and tone mapping. Our approach is based on a model of retinal processing of the human visual system (HVS). In the HVS, rendering operations, including adaptation, are performed directly on the cone responses, which corresponds to a mosaic image. Our workflow conforms more closely to the retinal processing model, performing all rendering before demosaicing.. This reduces the complexity of the computation, as only one third of the pixels are processed. This is especially important as our tone mapping operator applies local and global tone corrections, which is usually needed to well render high dynamic scenes. Our algorithms efficiently process HDR images with different keys and different content

Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions

In color gamut mapping of pictorial images, the lightness rendition of the mapped images plays a major role in the quality of the final image. For color gamut mapping tasks, where the goal is to produce a match to the original scene, it is important to maintain the perceived lightness contrast of the original image. Typical lightness remapping functions such as linear compression, soft compression, and hard clipping reduce the lightness contrast of the input image. Sigmoidal remapping functions were utilized to overcome the natural loss in perceived lightness contrast that results when an image from a full dynamic range device is scaled into the limited dynamic range of a destination device. These functions were tuned to the particular lightness characteristics of the images used and the selected dynamic ranges. The sigmoidal remapping functions were selected based on an empirical contrast enhancement model that was developed from the results of a psychophysical adjustment experiment. ..

Gamut extension algorithm development and evaluation for the mapping of standard image content to wide-gamut displays

Wide-gamut display technology has provided an excellent opportunity to produce visually pleasing images, more so than in the past. However, through several studies, including Laird and Heynderick, 2008, it was shown that linearly mapping the standard sRGB content to the gamut boundary of a given wide-gamut display may not result in optimal results. Therefore, several algorithms were developed and evaluated for observer preference, including both linear and sigmoidal expansion algorithms, in an effort to define a single, versatile gamut expansion algorithm (GEA) that can be applied to current display technology and produce the most preferable images for observers. The outcome provided preference results from two displays, both of which resulted in large scene dependencies. However, the sigmoidal GEAs (SGEA) were competitive with the linear GEAs (LGEA), and in many cases, resulted in more pleasing reproductions. The SGEAs provide an excellent baseline, in which, with minor improvements, could be key to producing more impressive images on a wide-gamut display

Appearance-based image splitting for HDR display systems

High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies