Won-Hee Choe

Factors affecting the psychophysical image quality evaluation of mobile phone displays: the case of transmissive liquid-crystal displays

Display manufacturers require new data and computational models that consider the effect of ambient illumination in order to develop higher-quality displays. In this study, typical variations of small-sized mobile LCDs that exist in the real world were first simulated using a device characterization technique. In addition, psychophysical attributes (e.g., naturalness, clearness, sharpness, contrast, colorfulness, and preference) affecting the image quality evaluation process were analyzed. Consequently, naturalness and clearness were found to be the most statistically significant psychophysical attributes for modeling image quality. As the ambient-illumination level was increased, the image quality was exponentially impaired and the contribution of clearness increased.

31.1: Development of Six Primary-Color LCD

We developed the multiple primary color LCDs where the pixels were composed of red(R), green(G), blue(B), cyan(C), magenta(M) and yellow(Y) subpixels. The color gamut was extended 99% of NTSC standard, while the brightness was also increased by 15%, compared to the conventional RGB three primary color LCDs. To get a natural color image we designed the color coordinates and luminance levels of each six subpixles, and developed color algorithm to convert the RGB signal to RGBCMY signal.

CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones

Digital images captured from CMOS image sensors suffer Gaussian noise and impulsive noise. To efficiently reduce the noise in Image Signal Processor (ISP), we analyze noise feature for imaging pipeline of ISP where noise reduction algorithm is performed. The Gaussian noise reduction and impulsive noise reduction method are proposed for proper ISP implementation in Bayer domain. The proposed method takes advantage of the analyzed noise feature to calculate noise reduction filter coefficients. Thus, noise is adaptively reduced according to the scene environment. Since noise is amplified and characteristic of noise varies while the image sensor signal undergoes several image processing steps, it is better to remove noise in earlier stage on imaging pipeline of ISP. Thus, noise reduction is carried out in Bayer domain on imaging pipeline of ISP. The method is tested on imaging pipeline of ISP and images captured from Samsung 2M CMOS image sensor test module. The experimental results show that the proposed method removes noise while effectively preserves edges.

Image-color-quality modeling under various surround conditions for a 2-in. mobile transmissive LCD

— This study aims to develop an image-color-quality (ICQ) model for a 2-in. mobile transmissive liquid-crystal display (LCD). A hypothetical framework for ICQ judgment was made to visually assess ICQ based the cognitive processes of the human visual system (HVS), and then an illumination adaptive ICQ model applicable for various surround conditions was developed. The memory color reproduction ratio (MCRR) of a locally adapted region of interest in a complex image reproduced on a mobile display was first computed. The colorfulness index and luminance contrast for all of the pixels in the image were then calculated by a global adaptation process. Finally, an ICQ model including all of the three attributes was developed under dark conditions using an assessed set of psychophysical data. The model gave more accurate performance than the mean accuracy for all of the observers. It was also visually tested under three different outdoor conditions, including overcast, bright, and very bright conditions, and the illuminance level range was from 7000 to 35,000 to 70,000 lx. The effect of outdoor illumination could be quantified as an exponential decay function and the ICQ model could be extended to cover a wide variety of outdoor illuminations conditions.

Color decomposition method for multiprimary display using 3D-LUT in linearized LAB space

This paper proposes a color decomposition method for a multi-primary display (MPD) using a 3-dimensional look-up-table (3D-LUT) in linearized LAB space. The proposed method decomposes the conventional three primary colors into multi-primary control values for a display device under the constraints of tristimulus matching. To reproduce images on an MPD, the color signals are estimated from a device-independent color space, such as CIEXYZ and CIELAB. In this paper, linearized LAB space is used due to its linearity and additivity in color conversion. First, the proposed method constructs a 3-D LUT containing gamut boundary information to calculate the color signals for the MPD in linearized LAB space. For the image reproduction, standard RGB or CIEXYZ is transformed to linearized LAB, then the hue and chroma are computed with reference to the 3D-LUT. In linearized LAB space, the color signals for a gamut boundary point are calculated to have the same lightness and hue as the input point. Also, the color signals for a point on the gray axis are calculated to have the same lightness as the input point. Based on the gamut boundary points and input point, the color signals for the input point are then obtained using the chroma ratio divided by the chroma of the gamut boundary point. In particular, for a change of hue, the neighboring boundary points are also employed. As a result, the proposed method guarantees color signal continuity and computational efficiency, and requires less memory.

Optimal chromaticities of the primaries for wide gamut 3-channel display

The theoretical approach is introduced to design the optimal chromaticities for primaries with a given size of triangular color gamut in xy-plane. Optimal primaries are defined as a set of chromaticities of red, green and blue primaries with fixed white point that most optimally satisfying four criteria, i.e. gamut size, gamut shape, coverage of object colors and hue of the primaries, in the visually uniform color space, CIECAM02. It is assumed that the optimal gamut should cover that of sRGB and have similar maximum chroma for each hue. The number of SOCS data located outside the gamut is used as a criterion to judge the coverage of object colors. Also it is set the hues of primaries to be close to those of sRGB. The simulation results showed that the optimal primaries for 85% of NTSC area have similar points with sRGB for red and blue, and green primary is located in between sRGB and NTSC. For 100% of NTSC area, the optimal chromaticities are located near those of NTSC for red and green and that of sRGB for blue.

Motion artifact-free HDR imaging under dynamic environments

High dynamic range (HDR) imaging is one of the most important emerging fields of the next generation digital cameras. It is hard to handle a problem so-called ghosting artifact caused by camera shake and/or object motion in the method of fusing a set of differently exposed images. Some object motions around under or over saturation region still produce severe artifacts due to the reference images dynamic range limitation. For the commercial product, it is the important problem to be solved completely. We analyze this problem and propose a new HDR deghosting scheme capable of dealing with various motions. In order to avoid the ghosting artifacts, we capture only two uncompressed Bayer raw images with different exposures, select the wider dynamic range image as a reference, and process them in the Bayer domain. The experimental results show that our proposed method provides motion artifact-free under dynamic environments with various moving objects.

A digital ISO expansion technique for digital cameras

Markets demands of digital cameras for higher sensitivity capability under low-light conditions are remarkably increasing nowadays. The digital camera market is now a tough race for providing higher ISO capability. In this paper, we explore an approach for increasing maximum ISO capability of digital cameras without changing any structure of an image sensor or CFA. Our method is directly applied to the raw Bayer pattern CFA image to avoid non-linearity characteristics and noise amplification which are usually deteriorated after ISP (Image Signal Processor) of digital cameras. The proposed method fuses multiple short exposed images which are noisy, but less blurred. Our approach is designed to avoid the ghost artifact caused by hand-shaking and object motion. In order to achieve a desired ISO image quality, both low frequency chromatic noise and fine-grain noise that usually appear in high ISO images are removed and then we modify the different layers which are created by a two-scale non-linear decomposition of an image. Once our approach is performed on an input Bayer pattern CFA image, the resultant Bayer image is further processed by ISP to obtain a fully processed RGB image. The performance of our proposed approach is evaluated by comparing SNR (Signal to Noise Ratio), MTF50 (Modulation Transfer Function), color error ∝E*ab and visual quality with reference images whose exposure times are properly extended into a variety of target sensitivity.

Exposure-adaptive Color Image Enhancement

In many cases, it is not possible to faithfully capture shadow and highlight image data of a high dynamic range (HDR) scene using a common digital camera, due to its narrow dynamic range (DR). Conventional solutions tried to solve the problem with an captured image which has saturated highlight and/or lack of shadow information. In this situation, we introduce a color image enhancing method with the scene-adaptive exposure control. First, our method recommends an optimal exposure to obtain more information in highlight by the histogram-based scene analysis. Next, the proposed luminance and contrast enhancement is performed on the captured image. The main processing consists of luminance enhancement, multi-band contrast stretching, and color compensation. The luminance and chrominance components of input RGB data is separated by converting into HSV color space. The luminance is increased using an adaptive log function. Multi-band contrast stretching functions are applied to each sub-band to enhance shadow and highlight at the same time. To remove boundary discontinuities between sub-bands, the multi-level low-pass filtering is employed. The blurred image data represents local illumination while the contrast-stretched details correspond to reflectance of the scene. The restored luminance image is produced by the combination of multi-band contrast stretched image and multilevel low-pass filtered image. Color compensation proportional to the amount of luminance enhancement is applied to make an output image.

Low light imaging system with? Expanding spectrum band for digital camera

This paper presents the camera system for increasing sensitivity especially in the low light condition. The camera system consists of two parts. The one is the unique image sensor structure which includes special pixels with broad band spectrum response (White+Near IR) and the other is image processing algorithm which fuses color and White+Near IR (WNIR) information to produce high sensitivity color image. With prototype system we prove the possibility of increasing sensitivity, which is almost 3 times better than conventional RGGB Bayer system.