Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials

Abstract

In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is presented. The compressor consists of a low-cost YEF color space converter and variable-length predictive with a combination of Golomb-Rice and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The compression algorithm is capable to work with white light imaging (WLI) and narrow band imaging (NBI) with average compression ratio of 78% and 84% respectively. Finally, a complete capsule endoscopy system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. The prototype is developed using circular PCBs having a diameter of 16 mm. Several in-vivo and ex-vivo trials using pig's intestine have been conducted using the prototype to validate the performance of the proposed lossless compression algorithm. The results show that, compared with all other existing works, the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression.

Performance of Predictive Coders for Wireless Capsule Endoscopy Image Compression

“…The input to the compression module is the endoscopic image and the output is the compressed bit stream. The encoder is designed to be two-stage with a DPCM coder and Golomb Rice coder [5]. In this work, DPCM coder is replaced by a few predictive coder.…”

“…It is reported to have developed a compression algorithm based on neighboring pixel correlation. DPCM based image compression algorithms [5] have lower complexity and do not need a buffer memory is highly acceptable. However, DPCM coders make using of immediate previous neighbors alone and this problem has not been given adequate attention in the literature.…”

“…The pre-processing stage involves converting the RGB image into YUV color space. The chrominance-based color spaces prove to be effective in reducing the representation and achieves data reduction [5]. The YUV model is preferred for the compression scenario as the variation of the component values in YUV representation is less compared to the RGB model [15].…”

“…Z denotes neighboring pixel in previous column adjacent to current pixel Z . The prediction error is then estimated by subtracting original pixel value from the predicted value as given in (5).…”

“…The design of a compression module is vital in wireless capsule endoscopy. Many criteria need to be addressed in this scenario [5]. Firstly, the bandwidth of the network which defines the level to which the amount of data in a video stream has to be minimized.…”