Until now, many of mobile display manufacturers try to improve the contrast ratio, viewing angle, and backlightluminance
for its color fidelity and image quality. However, with the multimedia convergence, various imaging devices
have been made smaller and loaded in a mobile phone as independent modules, which brings about the necessity of the
color consistency between each module. Especially, with the population and rapid growth of mobile camera, it is
important for mobile LCD to reproduce realistically and accurately the object color of a moving-picture transmitted by a
mobile camera. Therefore, we developed a real-time color matching system between mobile camera and mobile LCD
based on a 16-bit lookup table (LUT) design. As a result, a moving-picture is realistically and accurately reproduced on
mobile LCD by applying the proposed lookup table to mobile display.
This paper proposes a method of colorimetric characterization based on the color correlation between the distributions of colorant amounts in a CMYKGO printer. In colorimetric characterization beyond three colorants, many color patches with different combinations of colorant amounts can be used to represent the same tri-stimulus value. Therefore, choosing the proper color patches corresponding each tri-stimulus value is important for a CMYKGO printer characterization process. As such, the proposed method estimates the CIELAB value for many color patches, then selects certain color patches while considering high fidelity and the extension of the gamut. The selection method is divided into two steps. First, color patches are selected based on their global correlation, i.e. their relation to seed patches on the gray axis, and become the reference for correlation. However, even though a selected color patch may have a similar overall distribution to the seed patch, if the correlation factor is smaller than the correlation factors for neighboring patches, the color patch needs to be reselected. Therefore, in the second step, the color patch is reselected based on the local correlation with color patches that have a lower correlation factor with the seed patch. Thus, to reselect the color patch, the seed patch is changed to the average distribution of eight neighboring selected color patches, and the new color patch selected considering the new correlation factor. Consequently, the selected color patches have a similar distribution to their neighboring color patches. The selected color patches are then measured for accuracy, and the relation between the digital value and the tristimulus value for the color patches stored in a lookup table. As a result of this characterization, the gamut is extended in the dark regions and the color difference reduced compared to conventional characterization methods.
This paper proposes an adaptive error diffusion algorithm for text enhancement followed by an efficient text segmentation that uses the maximum gradient difference (MGD). The gradients are calculated along with scan lines, then the MGD values are filled within a local window to merge text segments. If the value is above a threshold, the pixel is considered as potential text. Isolated segments are then eliminated in a non-text region filtering process. After the text segmentation, a conventional error diffusion method is applied to the background, while edge enhancement error diffusion is used for the text. Since it is inevitable that visually objectionable artifacts are generated when using two different halftoning algorithms, gradual dilation is proposed to minimize the boundary artifacts in the segmented text blocks before halftoning. Sharpening based on the gradually dilated text region (GDTR) then prevents the printing of successive dots around the text region boundaries. The method is extended to halftone color images to sharpen the text regions. The proposed adaptive error diffusion algorithm involves color halftoning that controls the amount of edge enhancement using a general error filter. However, edge enhancement unfortunately produces color distortion, as edge enhancement and color difference are trade-offs. The multiplicative edge enhancement parameters are selected based on the amount of edge sharpening and color difference. Plus, an additional error factor is introduced to reduce the dot elimination artifact generated by the edge enhancement error diffusion. In experiments, the text of a scanned image was sharper when using the proposed algorithm than with conventional error diffusion without changing the background.
This paper proposes multi-level vector error diffusion based on adaptive primary color selection for fast and accurate color reproduction. Conventional bi-level vector error diffusion uses eight primary colors(R, G, B, C, M, Y, W, K). However, multi-level vector error diffusion uses more primary colors (this paper uses 64 primary colors) depending on the printing device, thereby significantly increasing the time complexity due to the additional increment of computation. Moreover, the output image can also include color artifacts that have a noticeable primary color under the influence of
large quantization error and increased primary color. Accordingly, to reduce these problems, we proposed the quantization process to decide a candidate primary among the 64 primary colors using lightness difference. First, we classified the 64 primary colors into 60 chromatic colors and 4 achromatic colors and then we exclude primary colors with the large lightness difference against the input color from a set of 60 chromatic primary colors. Using both 4 achromatic primary colors and a candidate primary colors, we calculated the vector norm to select output color. Also this paper determine optimal threshold experimentally to remove smear artifacts resulting from the diffusion of large quantization error. As a result, this paper archives fast multi-level vector error diffusion by avoiding additional computation and produces visually pleasing halftone pattern by excluding noticeable primary colors.