VLSI based orthogonal diagonal cross hair search (ODCHS) algorithm implementation for efficient image compression

Krishnaswamy, Ramachandran

doi:10.1016/j.micpro.2020.103114

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…SFC-based WP using the lifting scheme further reduces hardware resources for lowarea device priority applications. The lifting scheme called the second generation wavelet transform has lower computational complexity than the classic wavelet transforms but has disadvantages [22,41]. Sequential data processing in the lifting scheme increases the arithmetic operations delay decreases the transformation speed compared to parallel data processing in the classic wavelet transform.…”

Section: B Hardware Implementations Results Of Medical Image Wp With ...mentioning

confidence: 99%

“…An objective method for computing the optimum threshold value in the dual-tree complex WP based on real-time MR images denoising is proposed in [31]. Krishnaswamy [41] proposed an efficient image codec based on frequency domain transformation using lifting wavelet transform for efficient MRI compression. An algorithm for perceptually lossless volumetric CT image compression using fractional wavelet filtering is presented in [23].…”

Section: A Medical Image Processing Using Waveletsmentioning

confidence: 99%

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RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

et al. 2022

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Medical imaging using different modalities has many problems. The main ones are low informativeness, various distortion noises, and a large amount of information. Fusion, denoising, and visual data compression are used to solve them in practice. Discrete wavelet transform is one way to implement various fusion, denoising, and compression methods for 2D and 3D medical image processing. Medical imaging systems produce increasingly accurate images with scanning technology and digital devices development. These images have improved quality using both higher spatial resolutions and color bit-depth. Processing a large volume of medical imaging data requires considerable resources and processing time. Modern wavelet-based devices for medical image processing do not meet the current performance demand. Hardware accelerators are being designed to solve this problem. This paper proposes new (fieldprogrammable gate array) FPGA accelerators using wavelet processing (WP) with scaled filter coefficients (SFC) and parallel computing in residue number system (RNS) to improve the performance of high-quality 3D medical image WP systems. The computational complexity is reduced using the developed WP method with SFC and the proposed wavelet filter coefficients scaling algorithm. Parallel computing is organized in RNS using moduli sets of a particular type. Hardware implementation of 3D medical image WP using the proposed FPGA accelerators increases device performance by 2.89-3.59 times, increasing the hardware resources by 1.18-3.29 times compared to state-of-the-art solutions. The device performance improvement is achieved while maintaining high-quality 3D medical image processing in peak signal-to-noise ratio terms.INDEX TERMS Medical image processing, discrete wavelet transform, scaled filter coefficients, residue number system, high-performance computing, hardware accelerator, field-programmable gate arrays.

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Section: B Hardware Implementations Results Of Medical Image Wp With ...mentioning

confidence: 99%

Section: A Medical Image Processing Using Waveletsmentioning

confidence: 99%

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

et al. 2022

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“…In addition, the size of block is fixed in these techniques and even in BTC-DHT [26], while the architectures such as fuzzy-BTC with GR [27], JPEG-LS with IGR [28] International Journal of Intelligent Engineering and Systems, Vol. 15 considered variable size of block. However, due to the random selection of block size, the hardware resource utilization is bit high in terms of gate count, power consumption, and memory as demonstrated in Table 6, where the proposed QCA-IEGREC obtained effective hardware resource utilization with lesser gate count, and lower power consumption with improved performance of CR and FOM.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, various authors performed research on image compression methods without using any transformation methods and they have tried to improve CR as well. In [15], JPEG image compression technique was developed using orthogonal diagonal cross hair search. Later, DWT with tree-seed algorithm (TSA) is designed, where DWT is used for the division of images into 8×8 blocks [16].…”

Section: Introductionmentioning

confidence: 99%

Quantum-dot Cellular Automata Based Lossless CFA Image Compression Using Improved and Extended Golomb-rice Entropy Coder

2022

IJIES

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This article presents an efficient hardware architecture of lossless image compression mechanism using colour filter array (CFA) for wireless camera networks. Recently, Quantum-dot cellular automata (QCA) technology has been widely using in the field of VLSI, thus the proposed compressor is developed using QCA technology to mitigate the area, and power as compared to standard CMOS technology. In addition, an improved and extended Golomb-Rice entropy coder (IEGREC) is proposed to reduce the memory requirement, and computational complexity. The IEGREC approach eliminates the requirement of context module, which is a major component in lossless image compressor architectures and its memory to lessen the hardware resource utilization. Further, to maintain the pixel connectivity and higher compression ratio (CR), the proposed architecture utilizes the adaptive Golomb-Rice (GR) parameter prediction and control module. Finally, packer module is used to maintain the flow of output compressed bit stream. The extensive simulation results show that proposed QCA-IEGREC method performs superior as compared to state-of-art approaches in terms of lesser area as 3700 gate count, and reduced power of 1.02 mW, and even image quality statistics such as 18.78dB of peak signal-to-noise ratio (PSNR), 152.3 of figure-ofmerit (FOM), and 29.3 of CR.

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RETRACTED: FPGA medical big data system and ischemic stroke rehabilitation nursing

Liu

Zhai²,

Han

2021

Microprocessors and Microsystems

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VLSI based orthogonal diagonal cross hair search (ODCHS) algorithm implementation for efficient image compression

Cited by 5 publications

References 14 publications

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

Quantum-dot Cellular Automata Based Lossless CFA Image Compression Using Improved and Extended Golomb-rice Entropy Coder

RETRACTED: FPGA medical big data system and ischemic stroke rehabilitation nursing

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