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

Modelling Surround-aware Contrast Sensitivity for HDR Displays

Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state-of-the-art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surroundaware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone-mapping, and improved accuracy in visual difference prediction

Color appearance in high-dynamic-range imaging

When viewing images on a monitor, we are adapted to the lighting conditions of our viewing environment as well as the monitor itself, which can be very different from the lighting conditions in which the images were taken. As a result, our perception of these photographs depends directly on the environment in which they are displayed. For high-dynamic-range images, the disconnect in the perception of scene and viewing environments is potentially much larger than in conventional film and photography. To prepare an image for display, luminance compression alone is therefore not sufficient. We propose to augment current tone reproduction operators with the application of color appearance models as an independent preprocessing step to preserve chromatic appearance across scene and display environments. The method is independent of any specific tone reproduction operator and color appearance model ( CAM) so that for each application the most suitable tone reproduction operator and CAM can be selected

A Black-Point Adaption model for color reproduction

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

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

Traffic sign recognition based on human visual perception.

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions

Quantifying the colour appearance of displays.

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Traffic sign recognition based on human visual perception

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo