Color Image Quantization and Dithering Method Based on Human Visual System Characteristics

Recently, the use of color image data has been growing fast in the area of image processing.1–7 To express natural color in conventional low-cost color devices, the color image should be quantized for monitoring and dithered for printing. A video monitor displays a color image by modulating the intensity of three primary colors (red, green, and blue) at each pixel of the color image. In a digitized color image, each primary color is usually quantized with 8 bits of resolution to eliminate distinguishable quantization steps in trichromatic specification (luminance, hue, and saturation). Thus, full-color digital display systems use 24 bits to specify the color of each pixel on the screen. However, the cost of high-speed memory needed to support such a display on a high-resolution monitor makes many applications impractical. An alternative approach in currently available displays is to provide a limited number of bits, such as 8 bits, for specifying the color at each pixel. Each of these 28 values is then used as an index of a user-defined color table, i.e., color palette. Each entry in the table contains a 24-bit value that specifies each primary component of the color image. In this way, the user is allowed to select a small subset of the color palette from the full range of 224 colors. The drawback of this scheme is that it restricts the number of colors that may be simultaneously displayed.1–7 Because natural images typically contain a large number of distinguishable colors, displaying such images with a limited palette is difficult. Several techniques exist for color quantization, some of which are based on a more general class of vector quantization (VQ) techniques. One approach involves the iterative refinement of an initially New methods for both color palette design and dithering based on human visual system (HVS) characteristics are proposed. Color quantization for palette design uses the relative visual sensitivity and spatial masking effect of HVS. The dithering operation for printing uses nonlinear quantization, which considers the overlapping phenomena among neighbor printing dots, and then a modified dot-diffusion algorithm is followed to compensate the degradation produced in the quantization process. The proposed techniques can produce high-quality images in low-bit color devices.