This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in the non-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients and the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, a noise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach.

The human eye usually experiences a loss of color sensitivity when it is subjected to high levels of luminance, and perceives a discrepancy in color between high and normal-luminance displays, generally known as a hue shift. Accordingly, this paper models the hue-shift phenomenon and proposes a hue-correction method to provide perceptual matching between high and normal-luminance displays. To quantify the hue-shift phenomenon for the whole hue angle, 24 color patches with the same lightness are first created and equally spaced inside the hue angle. These patches are then displayed one-by-one on both displays with different luminance levels. Next, the hue value for each patch appearing on the high-luminance display is adjusted by observers until the perceived hue for the patches on both displays appear the same visually. After obtaining the hue-shift values from the color matching experiment, these values are fit piecewisely into seven sinusoidal functions to allow hue-shift amounts to be approximately determined for arbitrary hue values of pixels in a high-luminance display and then used for correction. Essentially, an input RGB image is converted to CIELAB LCh (lightness, chroma, and hue) color space to obtain the hue values for all the pixels, then these hue values are shifted according to the amount calculated by the functions of the hue-shift model. Finally, the corrected image is inversely converted to an output RGB image. For evaluation, a matching experiment was performed using several test images and z-score comparisons.

This paper proposes a colorimetric characterization method using the color correlation between the colorants in a hi-fi printer. While several colorant combinations can be used to match a certain color stimulus in a hi-fi printing system with more than 3 colorants, conventional colorimetric characterization methods only use 3 or 4 colorants to render a color, thereby limiting the color representation. As a result, the gamut is limited as they give up the other combinations of colorants. Therefore, this paper proposes a method of colorimetric characterization that uses combinations of all the colorants. As such, certain colorant combinations are selected based on considering the correlation factor between the colorant amount distributions. The correlation factor also affects the interpolation error, as the colorants are not independent of each other. Consequently, the total gamut is increased in low lightness regions, and the colors are represented more accurately.

Mobile displays such as PDAs and cellular phones can be placed under various illumination levels, different from the flat panel displays mainly used in the indoor environment. In particular, in the daylight condition, the displayed images or text on a mobile display can be darkly perceived, i.e., the degradation of sunlight-readability. In this paper, we consider why this kind of problem occurs in mobile phones and suggest the illumination level adaptive color reproduction method with lightness adaptation model and flare compensation.

Color signal decomposition method is to decompose the conventional three-primary colors (RGB) into the multi-primary control values of multi-primary display (MPD) under the constraints of tri-stimulus matching. For the satisfaction of tri-stimulus matching between input and output display systems, the MPD color signals have to be estimated from a device-independent color space such as CIEXYZ or CIELAB. However, as the target of MPD is to display moving-picture data, it is necessary to simplify the color space conversion between input and output systems and decomposition of the multi-primary control values. In this paper, color signal decomposition method for MPD is proposed using 3-dimensional look-up-table (3D-LUT) in linearized LAB space. The linearized LAB space satisfies the linearity and additivity in color space conversion and easily constructs the 3-D LUT considering lightness, chroma, and hue. For the reproduction of moving-picture data in MPD, the proposed method is based on 3-D LUT structure to reduce the complexity of hardware and processing time. First, 3-D LUT containing gamut boundary data of MPD is created to derive the multi-primary control values. The input images are transformed to linearized LAB values. Then, the corresponding color signals on the gamut boundary points, which have the same lightness and hue as the input point, are calculated. Also, the color signal for a point on the gray axis is calculated. Based on the gamut boundary points and a point on the gray axis, the MPD color signals for the input values can be obtained by interpolating between the gamut boundary points and a point on the gray axis. In particular, the neighboring gamut boundary points are used in the region of occurring abrupt signal change for a smooth change of hue. As a result, the proposed method guarantees computational efficiency and color signal continuity and requires less memory rather than conventional color decomposition methods.

This paper proposes a method for estimating the illuminant chromaticity using the distributions of camera responses obtained by a CCD camera in a real-world scene. Illuminant estimation using a highlight method is based on the geometric relation between a body and its surface reflection. In general, the pixels in a highlight region are affected by an illuminant geometric difference, camera quantization errors and the nonuniformity of the CCD sensor. As such, this leads to inaccurate results if an illuminant is estimated using the pixels of a CCD camera without any preprocessing. Accordingly, to solve this problem, the proposed method analyzes the distribution of the CCD camera responses and selects pixels using the Mahalanobis distance in highlight regions. The use of the Mahalanobis distance based on the camera responses enables the adaptive selection of valid pixels among the pixels distributed in the highlight regions. Lines are then determined based on the selected pixels with r-g chromaticity coordinates using a principal component analysis (PCA). Thereafter, the illuminant chromaticity is estimated based on the intersection points of the lines. Experimental results using the proposed method demonstrated a reduced estimation error compared with the conventional method.

This paper proposes an improved ray tracing technique that can reproduce a realistic image based on the spectral characteristic of real lights and objects. The proposed algorithm is able to generate a realistic image that simulates the physical quantity of light energy incoming toward a viewer. First, an accurate image is reproduced using the backward ray tracing method and by tracing the spectral transformation procedures of the rays using the spectral characteristic of objects and illuminants. Next, the shading model of the reproduction algorithm of a realistic image is improved by applying Bouguer-Beer's law to consider the optical absorptive property of transparent objects. Finally, a new ambient light term that considers the diffuse reflection of neighboring objects is defined instead of a constant ambient light term.

An important problem in color-based image retrieval and video segmentation is to lack information about how color is spatially distributed. To solve this problem and enhance the performance of image and video analyses, a spatial color descriptor is proposed involving a color adjacency histogram and color vector angle histogram. The color adjacency histogram represents the spatial distribution of color pairs at color edges in an image, thereby incorporating spatial information into the proposed color descriptor. Meanwhile, the color vector angle histogram represents the global color distribution of smooth pixels in an image. Since the proposed color descriptor includes spatial adjacency information between colors, it can robustly reduce the effect of a significant change in appearance and shape in image and video analyses. Moreover, since the color adjacency histogram is simply represented by binary streams, the storage space required for the image histogram values can be effectively reduced. Experimental results show that even with significant appearance changes, the proposed color descriptor could produce a high image retrieval rate and accurately detect abrupt scene-cuts in a video analysis.

The paper proposes a tone reproduction method using neural network and sigmoid function to perceptually compensate for the characteristics of an output device. The printer was used as an output device and its characteristics were analyzed by colorimetric measurement. The measured device characteristics were used in the learning process of a neural network which modeled the I/O characteristics of the device and the sigmoid function was exploited for optimal tone reproduction curves for the printer. In the application of real images, the mapping function between the proposed tone-curve and input signal of each device was built as a LUT (look-up table) between sigmoid function input to the neural network and output of the neural network. As a result, the proposed algorithms showed detailed color reproduction and enhanced image contrast on the printer.

New methods to improve the encoding of the connectivity and geometry of the topological surgery scheme are proposed. In connectivity compression, after obtaining the vertex and triangle spanning trees by decomposing a three-dimensional object, bits are adaptively allocated to each run of two spanning trees on a threshold basis. The threshold is the length of a binary number of the maximum run length. If a run length exceeds the threshold, it is represented by a binary number of the run length. Otherwise, it is represented by a bit sequence. Therefore, compression efficiency is enhanced through an adaptive bit allocation to each run of two spanning trees. In geometry compression, since vertices represented by three-dimensional vectors are stored according to the order of the travelling along vertex spanning tree by depth-first searching, they have geometrical closeness. The geometry compression efficiency can be improved if the local characteristics of vectors are considered. Therefore, dynamic finite state vector quantization, which has subcodebooks depending on a local characteristic of vectors, is used to encode the geometry information. As it dynamically constructs a subcodebook by predicting an input vector's state, it produces less distortion and gives better visual quality than conventional methods.

In this paper, a new light-value approaching method to enhance a color image by excluding the effect of incident illumination is proposed. The method uses the highlight-presenting vector transformation, in which the estimated color of illumination is transformed to the white color of natural daylight while preserving the highlight. In the method, the initial transformation matrix using simple vector rotation is corrected by regression, and the corrected transformation matrix is applied to input color. Then the transformed red, green, and blue values of each pixel are nonlinearly mapped to the 8 bit values to be displayed on a natural color output device. The proposed method can produce the enhanced color image fast and efficiently without any space conversion or noticeable distortion.

Color quantization is to design a color palette having almost no noticeably perceived difference between original and quantized images. In this paper, to design such a palette, the color quantization algorithm is considered in two parts. The selection of a proper distortion measure and the design of an optimal palette. The proper selection of distortion measure in the quantization is important to image quality. Since the human eye is the final judge of image quality, it is desirable to use a perception-based distortion measure. Thus we developed an activity-weighted distortion measure considering color visual sensitivity and absorbance of human visual system (HVS) depending on each color component in the local region of image. Then using the distortion measure, a hierarchical quantization algorithm is proposed. The algorithm consists of initial and subdivision steps both to reduce computation time and to minimize the distortion based on spatial masking effect of HVS. The experimental results show that the proposed algorithm shows better visual quality and less computation time comparing to the conventional algorithms.