Study on Low-Power Image Processing for Gastrointestinal Endoscopy [chapter]

Meng-Chun Lin
2012 VLSI Design  
12 www.intechopen.com 2 Will-be-set-by-IN-TECH Study on Low-Power Image Processing for Gastrointestinal Endoscopy 3 the task of smoothing or sharpening. Therefore, we need a flexible ROF hardware to arbitrarily select wanted rank values into the operation procedure of LUM filter and we have proposed an architecture based on a maskable memory for rank-order filtering. The maskable memory structure, called dual-cell random-access memory (DCRAM), is an extended SRAM structure with maskable
more » ... s and dual cells. This dissertation is the first literature using maskable memory to realize ROF. Driving by the generic rank-order filtering algorithm, the memory-based architecture features high degree of flexibility and regularity while the cost is low and the performance is high. This architecture can be applied for arbitrary ranks and a variety of ROF applications, including recursive and non-recursive algorithms. Except efficiently eliminating annoying impulsive noises and enhance sharpness for GI images, the processing speed of ROF can also meet the real-time image applications. GICam image compressor The review of GICam image compression algorithm Instead of applying state-of-the-art video compression techniques, we proposed a simplified image compression algorithm, called GICam. Traditional compression algorithms employ the YCbCr quantization to earn a good compression ratio while the visual distortion is minimized, based on the factors related to the sensitivity of the human visual system (HVS). However, for the sake of power saving, our compression rather uses the RGB quantization (15) to save the computation of demosaicking and color space transformation. As mentioned above, the advantage of applying RGB quantization is two-fold: saving the power dissipation on preprocessing steps and reducing the computing load of 2-D DCT and quantization. Moreover, to reduce the hardware cost and quantization power dissipation, we have modified the RGB quantization tables and the quantization multipliers are power of two's. In GICam, the Lempel-Ziv (LZ) coding (18) is employed for the entropy coding. The reason we adopted LZ coding as the entropy coding, is because the LZ encoding does not need look-up tables and complex computation. Thus, the LZ encoding consumes less power and uses smaller silicon size than the other candidates, such as the Huffman encoding and the arithmetic coding. The target compression performance of the GICam image compression is to reduce image size by at least 75%. To meet the specification, given the quantization tables, we exploited the cost-optimal LZ coding parameters to meet the compression ratio requirement by simulating with twelve tested endoscopic pictures shown in Fig.3 . When comparing the proposed image compression with the traditional one in (11), the power consumption of GICam image compressor can save 98.2% because of the reduction of memory requirement. However, extending the utilization of battery life for a capsule endoscope remains an important issue. The memory access dissipates the most power in GICam image compression. Therefore, in order to achieve the target of extending the battery life, it is necessary to consider how to efficiently reduce the memory access. Analysis of sharpness sensitivity in gastrointestinal images The distributions of primary colors in the RGB color space In the modern color theory (16; 17), most color spaces in used today are oriented either toward hardware design or toward product applications. Among these color spaces, the 245 Study on Low-Power Image Processing for Gastrointestinal Endoscopy www.intechopen.com 4 Will-be-set-by-IN-TECH RGB(red, green, blue) space is the most commonly used in the category of digital image processing; especially, broad class of color video cameras and we consequently adopt the RGB color space to analyze the importance of primary colors in the GI images. In the RGB color space, each color appears in its primary spectral components of red, green and blue. The RGB color space is based on a Cartesian coordinate system, in which, the differ colors of pixels are points on or inside the cube based on the triplet of values (R, G, B). Due to this project was supported in part by Chung-Shan Institute of Science and Technology, Taiwan, under the project BV94G10P. The responsibility of Chung-Shan Institute of Science and Technology mainly designs a 512-by-512 raw image sensor. The block-based image data can be sequentially outputted via the proposed locally-raster-scanning mechanism for this raw image sensor. The reason for adopting a novel image sensor without using generally conventional ones is to efficiently save the size of buffer memory. Conventional raw image sensors adopt the raster-scanning mechanism to output the image pixels sequentially, but they need large buffer memory to form each block-based image data before executing the block-based compression. However, we only need a small ping-pong type memory structure to directly save the block-based image data from the proposed locally-raster-scanning raw image sensor. The structure of this raw image sensor is shown in Fig.1 (a) and the pixel sensor architecture for the proposed image sensor is shown in Fig.1 (b) . In order to prove the validity for this novel image sensor before the fabrication via the Chung-Shan Institute of Science and Technology, the chip of the 32-by-32 locally-raster-scanning raw image sensor was designed by full-custom CMOS technology and this chip is submitted to Chip Implementation Center (CIC), Taiwan, for the fabrication. Fig.2 (a) and Fig.2 (b) respectively shows the chip layout and the package layout with the chip specification. The advantage of this novel CMOS image sensor can save the large area of buffer memory. The size of buffer memory can be as a simple ping-pong memory structure shown in Fig.9 while executing the proposed image algorithm, a novel block coding. Our research only focuses on developing the proposed image compressor and other components are implemented by other research department for the GICam-II capsule endocopy. Therefore, the format of the GI image used in the simulation belongs to a raw image from the 512-by-512 sensor designed by Chung-Shan Institute of Science and Technology. In this work, we applied twelve GI images captured shown in Fig.3 for testcases to evaluate the compression technique. The distribution of GI image pixels in the RGB color space is non-uniform. Obviously, the GI image is reddish and the pixels are amassed to the red region. Based on the observation in the RGB color space, the majority of red values are distributed between 0.5 and 1 while most of the green and blue values are distributed between 0 and 0.5 for all tested GI images. To further analyze the chrominance distributions and variations in the RGB color space for each tested GI images, two quantitative indexes are used to quantify these effects. The first index is to calculate the average distances between total pixels and the maximum primary colors in each GI image, and the calculations are formulated as Eq.1, Eq.2 and Eq.3. First, Eq.1 defines the the average distance between total pixels and the most red color (R), in which, R(i, j) means the value of red component of one GI image at (i, j) position and the value of most red color (R max ) is 255. In addition, M and N represent the width and length for one GI image, respectively. The M is 512 and the N is 512 for twelve tesed GI images in this work. Next, Eq.2 also defines the average distance between total pixels and the most green color (G) and the value of most green one (G max )i s 255. Finally, Eq.3 defines the average distance between total pixels and the most blue color (B) and the value of most blue color (B max ) is 255. Table 1 shows the statistical results of R, G and B 246 VLSI Design
doi:10.5772/33771 fatcat:zdnuio6dmzchjn5jf3aej5mjbe