An ultra-low-power image compressor for capsule endoscope
© Lin et al; licensee BioMed Central Ltd. 2006
Received: 02 November 2005
Accepted: 25 February 2006
Published: 25 February 2006
Gastrointestinal (GI) endoscopy has been popularly applied for the diagnosis of diseases of the alimentary canal including Crohn's Disease, Celiac disease and other malabsorption disorders, benign and malignant tumors of the small intestine, vascular disorders and medication related small bowel injury. The wireless capsule endoscope has been successfully utilized to diagnose diseases of the small intestine and alleviate the discomfort and pain of patients. However, the resolution of demosaicked image is still low, and some interesting spots may be unintentionally omitted. Especially, the images will be severely distorted when physicians zoom images in for detailed diagnosis. Increasing resolution may cause significant power consumption in RF transmitter; hence, image compression is necessary for saving the power dissipation of RF transmitter. To overcome this drawback, we have been developing a new capsule endoscope, called GICam.
We developed an ultra-low-power image compression processor for capsule endoscope or swallowable imaging capsules. In applications of capsule endoscopy, it is imperative to consider battery life/performance trade-offs. Applying state-of-the-art video compression techniques may significantly reduce the image bit rate by their high compression ratio, but they all require intensive computation and consume much battery power. There are many fast compression algorithms for reducing computation load; however, they may result in distortion of the original image, which is not good for use in the medical care. Thus, this paper will first simplify traditional video compression algorithms and propose a scalable compression architecture.
As the result, the developed video compressor only costs 31 K gates at 2 frames per second, consumes 14.92 mW, and reduces the video size by 75% at least.
Gastrointestinal (GI) endoscopy has been popularly applied for the diagnosis of diseases of the alimentary canal including Crohn's Disease, Celiac disease and other malabsorption disorders, benign and malignant tumors of the small intestine, vascular disorders and medication related small bowel injury. There exist two classes of GI endoscopy; wired active endoscopy and wireless passive capsule endoscopy. The wired active endoscopy can enable efficient diagnosis based on real images and biopsy samples; however, it causes patients discomfort and pain to push flexible, relatively bulky cables into the digestive tube. To relief the suffering of patients, wireless passive capsule endoscopes are being developed worldwide [1–4]. The capsule moves passively through the internal GI tract with the aid of peristalsis and transmits images of the intestine wirelessly.
The scope of this paper is the design of an image compression processor for capsule endoscopes. Instead of applying existing compression standards, we developed simplified image compression specifically for capsule endoscopes. Unlike the general image compression techniques, the proposed image compression starts from raw images in the format of Bayer patterns and processes R, G1, G2, and B signals separately. Comparing with the traditional image compression, the proposed image compression is low-powered for three reasons. First, the proposed image compression does not need demosaicking, and hence saves the computing power of interpolation steps. Second, the proposed compression starts from the raw image, and does not need inner product operations for color-space transformation. Finally, the computation load of the 8-by-8 discrete cosine transform (DCT) can be reduced by the factor of 3.
The proposed image compression algorithm
Traditional image compression algorithms use the optimized quantization for YCbCr image to reduce compressed image size while the visual distortion is low. In order to quantize YCbCr image, the typical image compression requires two preprocessing steps that are demosaicking and the color space transformation. However, the demosaicking step requires weighted sums for color interpolation and the color space transformation requires calculation of inner products. From the view point of GICam, it is not worth it to dissipate power for both preprocessing steps as long as the compression quality and ratio are acceptable. The measure of compression quality is the peak signal-to-noise ratio (PSNR). The calculation of PSNR is formulated as Eq. (1):
Where MSE is the mean square error of decompressed image. The compression ratio (CR) is defined as the ratio of the raw image size to the compressed image size. The measure of the compression ratio is the compression rate. The formula of the compression rate is calculated by Eq. (2):
compression rate = (1-CR-1) × 100% (2)
In GICam, the Lempel-Ziv (LZ) coding  is employed for the entropy coding. The reason why we adopted the LZ coding as the entropy coding is that the LZ encoding does not need look-up tables and complex computation. Thus, the LZ encoding can consume less power and use smaller silicon size than the other candidates, such as the Huffman encoding and the arithmetic coding.
Architecture design and implementation of GIcam image compressor
This paper presents an ultra-low-power image compression processor for capsule endoscope or swallowable imaging capsules. In applications of capsule endoscopy, it is imperative to consider battery life/performance trade-offs. Instead of applying state-of-the-art video compression techniques, we propose an RGB-based compression algorithm in which the memory size and computational load can be significantly reduced. We first simplified traditional video compression algorithms by removing the color-space transformation. As shown in the result, the developed video compressor only costs 31 K gates at 2 frames per second, consumes 14.92 mW, and reduces the video size by 75% at least.
This work was supported in part by Chung-Shan Institute of Science and Technology, Taiwan, under the project BV94G10P and the National Science Council, R.O.C., under the grant number NSC 94-2220-E-009-023. The authors would like to thank National Chip Implementation Center(CIC) for technical support.
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