Human Visual System(HVS)의 특성과 image의 textural regions의 roughness을 이용하여 image segmentation을 행하여 high compression에서도 고화질을 나타내는 새로운 image coder를 이 논문에서 논한다. 제안된 image coder는 constant segments를 가진 segmentation-based image coding technique의 문제들을 다음과 같은 방법론을 제안함으로써 해결하였다. Image를 HVS으로 보았을 때 degree of roughness에 관하여 textually homogeneous regions으로 segmentation하였다. Fractal dimension을 roughness of textural regions을 측정하기 위하여 사용하였다. Segmentation은 fractal dimension을 thresholding하여 textural regions이 three texture classes로 분류하였다(perceived constant intensity, smooth texture, and rough texture). High compression을 가지는 고질화의 image coder는 각각의 segment boundary와 각각의 texture class에 효율적인 coding technique를 적용 함으로 얻었다.
A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The fractal dimension is used to measure the roughness of the textural regions. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. For the boundaries, a binary image representing all the boundaries is created. For regions belonging to perceived constant intensity, only the mean intensity values need to be transmitted. The smooth and rough texture regions are modeled first using polynomial functions, so only the coefficients characterizing the polynomial functions need to be transmitted. The bounda-ries, the means and the polynomial functions are then each encoded using an errorless coding scheme. Good quality reconstructed images are obtained with about 0.08 to 0.3 bit per pixel for three different types of imagery ; a head and shoulder image with little texture variation, a complex image with many edges, and a natural outdoor image with highly textured areas.
