Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy

Khan, Tareq; Wahid, Khan A.

doi:10.1155/2011/343787

Cited by 43 publications

(27 citation statements)

References 20 publications

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“…Julien et al in [47][48][49][50][51], leading to a 1-D bivariate encoded Arai DCT algorithm by Wahid and Dimitrov [37,41,42,52]. Recently, subsequent contributions by Wahid et al (using bivariate encoded 1-D Arai DCT blocks for row and column transforms of the 2-D DCT) has led to practical area-efficient VLSI video processing circuits with low-power consumption [53][54][55]. We now briefly summarize the state-of-the-art in both 1-D and 2-D DCT VLSI cores based on conventional fixed-point arithmetic as well as on AI encoding.…”

Section: Reviewmentioning

confidence: 99%

“…The following AI-based realizations of 2-D DCT computation relies on the row-and column-wise application of 1-D DCT cores that employ AI quantization [47][48][49][50][51]. The architectures proposed by Wahid et al rely on the lowcomplexity Arai Algorithm and lead to low-power realizations [41,42,[52][53][54]. However, these realizations also are based on repeated application along row and columns of an fundamental 1-D DCT building block having an FRS section at the output stage.…”

Section: Ai-based Dct Vlsi Circuitsmentioning

confidence: 99%

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A Row-Parallel 8$\,\times\,$8 2-D DCT Architecture Using Algebraic Integer-Based Exact Computation

Madanayake

Cintra²,

Onen³

et al. 2012

IEEE Trans. Circuits Syst. Video Technol.

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An algebraic integer (AI) based time-multiplexed row-parallel architecture and two final-reconstruction step (FRS) algorithms are proposed for the implementation of bivariate AI-encoded 2-D discrete cosine transform (DCT). The architecture directly realizes an error-free 2-D DCT without using FRSs between row-column transforms, leading to an 8×8 2-D DCT which is entirely free of quantization errors in AI basis. As a result, the user-selectable accuracy for each of the coefficients in the FRS facilitates each of the 64 coefficients to have its precision set independently of others, avoiding the leakage of quantization noise between channels as is the case for published DCT designs. The proposed FRS uses two approaches based on (i) optimized Dempster-Macleod multipliers and (ii) expansion factor scaling. This architecture enables low-noise high-dynamic range applications in digital video processing that requires full control of the finite-precision computation of the 2-D DCT. The proposed architectures and FRS techniques are experimentally verified and validated using hardware implementations that are physically realized and verified on FPGA chip. Six designs, for 4-and 8-bit input word sizes, using the two proposed FRS schemes, have been designed, simulated, physically implemented and measured. The maximum clock rate and block-rate achieved among 8-bit input designs are 307.787 MHz and 38.47 MHz, respectively, implying a pixel rate of 8×307.787≈2.462 GHz if eventually embedded in a real-time video-processing system. The equivalent frame rate is about 1187.35 Hz for the image size of 1920×1080. All implementations are functional on a Xilinx Virtex-6 XC6VLX240T FPGA device.

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Section: Reviewmentioning

confidence: 99%

Section: Ai-based Dct Vlsi Circuitsmentioning

confidence: 99%

A Row-Parallel 8$\,\times\,$8 2-D DCT Architecture Using Algebraic Integer-Based Exact Computation

Madanayake

Cintra²,

Onen³

et al. 2012

IEEE Trans. Circuits Syst. Video Technol.

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“…Region identification and segmentation techniques such as super pixel method [26] and Octree-based convolutional neural networks (O-CNN) [27] identify the region of interest. Video and image compression techniques such as JPEG [28], wavelet transform [29], statistic prediction scheme [30], hardware-based approach [31], Differential pulse code modulation (DPCM) [32,33], colour spaced based approaches [34][35][36], video code based approach [37] compresses the data to make it transfer and storage friendly. In this study, our interest was to explore various computer vision and machine learning technique used in existing CAD systems for abnormality detection in CE.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

Jani*,

Srivastava,

Srivastava

2019

IJRTE

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Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

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“…These images were used for the evaluation of our compression algorithms. Other systems [4,11,12] and [13] used these images in their experiments. So, the results could be able to be compared.…”

Section: Endoscopic Image Datasetmentioning

confidence: 99%

“…There have been a lot of works reported in image compression for WCE. Khan et al [4], presented a lossless compression algorithm based in predictive coding. Colour conversion is used and the error produced from the conversion is encoded by Golomb-rice encoder and Unary coding.…”

Section: Introductionmentioning

confidence: 99%

An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy

Intzes

Meng

Cosmas

2020

Sensors

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Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance.

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Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy

Cited by 43 publications

References 20 publications

A Row-Parallel 8$\,\times\,$8 2-D DCT Architecture Using Algebraic Integer-Based Exact Computation

A Row-Parallel 8$\,\times\,$8 2-D DCT Architecture Using Algebraic Integer-Based Exact Computation

Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy

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