Image representation and compression for capsule endoscope robot

Hu, Chao; Meng, Max Q.-H.; Liu, Li; Pan, Yinzi; Li, Yong

doi:10.1109/icinfa.2009.5204976

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…Here, we see that the proposed compression algorithm is the only lossless compression algorithm which produces infinite PSNR of the reconstructed images. In terms of compression ratio, the proposed algorithm outperforms [14,15,26,27]. It should be noted that, the work in [9,11,25,28] use DCTbased approach, which is computationally very expensive.…”

Section: Proposed Compression Algorithmmentioning

confidence: 99%

“…Comparing Tables 2 and 3, we see that the proposed compression algorithm works much better on WCE images than standard images. It is due to the fact that there are relatively large variations in U and V component in nonendoscopic images [14] 56.7 46.4 Xiang et al [15] 72.7 46.8 Wahid et al [9] 87.1 32.9 Turcza and Duplaga [10] 32.0 36.5 Lin et al [11] 79.6 32.5 JPEG-FP [9,25] 81.5 31.5 Hu et al [26] 72.0 (Q level = 2) 39.6 Wu and Li [27] 50.0 31.0 Dung et al [28] 82.0 36.2 Figure 7 and the use of unary code cannot produce good compression ratio as unary code becomes very large for large values as shown in Figure 8.…”

Section: Proposed Compression Algorithmmentioning

confidence: 99%

“…Transform coding (DCT) 4 × 4 R G B Y e s N o O(n log n) Turcza and Duplaga [10] Transform coding (DCT) [9,25] Transform coding (DCT) 4 × 4 R G B Y e s N o O(n log n) Hu et al [26] Wavelet transform coding 4 × 4 R G B Y e s N o O(n) Wu and Li [27] Block-based compressed sensing…”

Section: Copmpelxity Analysismentioning

confidence: 99%

“…The proposed algorithm does not implement the "Run mode" due to fact that endoscopic images do not contain long runs. The work in [26] uses a wavelet-based 8 VLSI Design [14] JPEG-LS (with run mode, NEAR>0) [15] JPEG-LS (with run mode, NEAR>0)…”

Section: Copmpelxity Analysismentioning

confidence: 99%

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Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy

Khan

Wahid

2011

VLSI Design

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We present a lossless and low-complexity image compression algorithm for endoscopic images. The algorithm consists of a static prediction scheme and a combination of golomb-rice and unary encoding. It does not require any buffer memory and is suitable to work with any commercial low-power image sensors that output image pixels in raster-scan fashion. The proposed lossless algorithm has compression ratio of approximately 73% for endoscopic images. Compared to the existing lossless compression standard such as JPEG-LS, the proposed scheme has better compression ratio, lower computational complexity, and lesser memory requirement. The algorithm is implemented in a 0.18 μm CMOS technology and consumes 0.16 mm × 0.16 mm silicon area and 18 μW of power when working at 2 frames per second.

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Section: Proposed Compression Algorithmmentioning

confidence: 99%

Section: Proposed Compression Algorithmmentioning

confidence: 99%

Section: Copmpelxity Analysismentioning

confidence: 99%

Section: Copmpelxity Analysismentioning

confidence: 99%

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Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy

Khan

Wahid

2011

VLSI Design

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“…Increasing resolution may alleviate the second problem; however, it will result in significant power consumption in RF transmitter. Hence, applying image compression is 2 EURASIP Journal on Advances in Signal Processing necessary for saving the power dissipation of RF transmitter [12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Subsample-Based Low-Power Image Compressor for Capsule Gastrointestinal Endoscopy

Lin

Dung

2011

EURASIP J. Adv. Signal Process.

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In the design of capsule endoscope, the trade-offs between battery-life and video-quality is imperative. Typically, the resolution of capsule gastrointestinal (GI) image is limited for the power consumption and bandwidth of RF transmitter. Many fast compression algorithms for reducing computation load; however, they may result in a distortion of the original image, which is not suitable for the use of medical care. This paper presents a novel image compression for capsule gastrointestinal endoscopy, called GICam-II, motivated by the reddish feature of GI image. The reddish feature makes the luminance or sharpness of GI image sensitive to the red component as well as the green component. We focus on a series of mathematical statistics to systematically analyze the color sensitivity in GI images from the RGB color space domain to the two-dimensional discrete-cosine-transform spatial frequency domain. To reduce the compressed image size, GICam-II downsamples the blue component without essential loss of image detail and also subsamples the green component from the Bayer-patterned image. From experimental results, the GICam-II can significantly save the power consumption by 38.5% when compared with previous one and 98.95% when compared with JPEG compression, while the average peak signal-to-noise ratio of luminance (PSNRY) is 40.73 dB.

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