Kuang-Tsu Shih

Single Image Realism Assessment and Recoloring by Color Compatibility

In this paper, we investigate the assessment of image realism by focusing our attention on the color compatibility between an inserted object and the background in an image composite. We propose a technique based on two color compatibility properties to achieve realistic image composition. The first property is related to the color similarity, and the second one to the consistence of color tendency between image regions. We further propose algorithms based on these two properties for image realism assessment and recoloring. These algorithms only require information from the image to be tested, making them suitable for practical applications where real images are unavailable. Effectiveness of the algorithms is demonstrated through various images and verified by ground truth.

Exploiting Perceptual Anchoring for Color Image Enhancement

The preservation of image quality under various display conditions becomes more and more important in the multimedia era. A considerable amount of effort has been devoted to compensating the quality degradation caused by dim LCD backlight for mobile devices and desktop monitors. However, most previous enhancement methods for backlight-scaled images only consider the luminance component and overlook the impact of color appearance on image quality. In this paper, we propose a fast and elegant method that exploits the anchoring property of human visual system to preserve the color appearance of backlight-scaled images as much as possible. Our approach is distinguished from previous ones in many aspects. First, it has a sound theoretical basis. Second, it takes the luminance and chrominance components into account in an integral manner. Third, it has low complexity and can process 720p high-definition videos at 35 frames per second without flicker. The superior performance of the proposed method is verified through psychophysical tests.

Color enhancement based on the anchoring theory

Providing consistent viewing experience across different reproduction conditions is an important issue for multimedia systems in the real world. In this paper, we propose a method to improve the perceptual quality of color reproduction for backlight-scaled images displayed on a liquid crystal display. Supported by the anchoring theory of lightness perception developed in psychology, this method is able to enhance the color appearance of images even when the backlight is only 5% of the original intensity. The goal is to make the appearance of the resulting images as close as possible to the original images illuminated with full backlight. The concept behind the proposed color enhancement method is general enough for many other applications. The effectiveness of the method is verified by subjective experiments.

Color enhancement for portable LCD displays in low-power mode

Switching the backlight of handheld devices to low power mode saves energy but affects the color appearance of an image. In this paper, we consider the chroma degradation problem and propose an enhancement algorithm that incorporates the CIECAM02 appearance model to quantitatively characterize the problem. In the proposed algorithm, we enhance the color appearance of the image in low power mode by weighted linear superposition of the chroma of the image and that of the estimated dim-backlight image. Subjective tests are carried out to determine the perceptually optimal weighting and prove the effectiveness of our framework.

Analysis of Disparity Error for Stereo Autofocus

As more and more stereo cameras are installed on electronic devices, we are motivated to investigate how to leverage disparity information for autofocus. The main challenge is that stereo images captured for disparity estimation are subject to defocus blur unless the lenses of the stereo cameras are at the in-focus position. Therefore, it is important to investigate how the presence of defocus blur would affect stereo matching and, in turn, the performance of disparity estimation. In this paper, we give an analytical treatment of this fundamental issue of disparity-based autofocus by examining the relation between image sharpness and disparity error. A statistical approach that treats the disparity estimate as a random variable is developed. Our analysis provides a theoretical backbone for the empirical observation that, regardless of the initial lens position, disparity-based autofocus can bring the lens to the hill zone of the focus profile in one movement. The insight gained from the analysis is useful for the implementation of an autofocus system.

Blocking harmful blue light while preserving image color appearance

Recent study in vision science has shown that blue light in a certain frequency band affects human circadian rhythm and impairs our health. Although applying a light blocker to an image display can block the harmful blue light, it inevitably makes an image look like an aged photo. In this paper, we show that it is possible to reduce harmful blue light while preserving the blue appearance of an image. Moreover, we optimize the spectral transmittance profile of blue light blocker based on psychophysical data and develop a color compensation algorithm to minimize color distortion. A prototype using notch filters is built as a proof of concept.

Anti-aliasing for light field rendering

Ghosting artifact resulted from angular undersampling is a major issue of light field rendering. In this paper, we propose an anti-aliasing method that compensates the effect of undersampling by making use of depth information. It can be incorporated into most existing refocusing algorithms to render high-quality images from light field. The method yields proper anti-aliasing across all depth range. Mathematical derivation of the method and the physical intuition behind the idea are described. Results are shown to demonstrate the performance of the method compared to previous ones.

Analysis of the effect of calibration error on light field super-resolution rendering

Light field photography, which has recently drawn considerable attention, provides novel functionalities such as refocusing and depth estimation at the same time. However, the resolution of the rendered refocus image is incomparably lower than the number of light ray samples in the light field. In this paper, we show that super-resolution can be performed to bridge the gap and that deconvolution, which is missing in most previous methods, is essential to the success of light field super-resolution rendering. In addition, we investigate the effect of four different kinds of camera calibration error on the quality of rendered images. Given an expected level of image quality, the upperbound of the camera calibration error is analyzed.

Enhancing portable LCD color in low-power mode

Handheld electronic devices, such as smart phones, have become increasingly popular. However, their LCD backlight consumes a significant amount of energy. While reducing the backlight power prolongs battery life1, it does so at the cost of degrading both luminance and chrominance image quality. Existing image-enhancement algorithms1–6 deal mostly with the luminance appearance, while color has been largely ignored. As a result, we are focusing our work on the chrominancedegradation problem. We investigated the relation between perceptual attributes and backlight intensity, modeled the effect of backlight using a color appearance model7, and plotted the chroma and saturation versus backlight intensity (see Figure 1). Chroma drops dramatically when we lower the backlight intensity. As a result, the gamut shrinks (see Figure 2). This observation serves as the basis of our algorithm, which aims to preserve the image’s chroma. In the flowchart of our algorithm (see Figure 3), we mixed the two chroma layers using the following equation: