M-ABRC (Adaptive Binary Range Coder) using Virtual Sliding Window technique and its VLSI implementation

Mrudula, Sunkara Teena; Murthy, K. E. Srinivasa; Prasad, M. N. Giri

doi:10.1016/j.micpro.2019.102901

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The main aim of image compression is to reduce the storage and transmission problems that occur in various fields handling the multimedia contents. Image compression schemes are developed to reduce the high-resolution images to cope up with the low resolution supported devices [2]. The major challenge in the image compression process is preserving the class of the image and sharpness of the image even after decompression.…”

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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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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RETRACTED: Light music online system based on FPGA and Convolutional Neural Network

Zhao

2021

Microprocessors and Microsystems

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VLSI implementation of image compressor using probabilistic run length coding

Boddu

Mandal

2023

Expert Systems

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Multimedia applications, such as image processing including image and video transfer, heavily rely on reduction. The traditional device methods to picture reduction use more space, energy, and processing time. The majority of current efforts use the Golomb‐Rice encoding, due to its larger memory requirement and higher computing difficulty. So, this research concentrated on hardware design‐oriented probability run length (PRL) coding technique based on lossless colour image compression. The block truncation coding (BTC) features of the compression process are used by the suggested PRL method. The proposed image compression hardware consists of various modules such as a Parameter calculator, fuzzy table, bitmap generator, BTC parameters training, prediction, and error control, and PRL‐based finite state machine (PRL‐FSM). The proposed image compressor utilizes the parameter calculator block, which estimates the block type based on the image pixel intensities for each sub‐block. Thus, each block of the image is compressed by using a new block type and generates a variable block size. The proposed method utilizes the PRL‐BTC encoding method, which also calculates the probability of error between the compressed image to the test image. The process is iterated until the performance trade‐off between hardware cost and compression ratio (CR) is achieved. Hence, both smooth regions and non‐smooth regions of images are perfectly compressed by the probability‐based block selection. The simulation results show that the proposed method resulted in a better area, power, delay metrics, peak signal‐to‐noise ratio (PSNR), and CR compared to the state of art approaches.

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M-ABRC (Adaptive Binary Range Coder) using Virtual Sliding Window technique and its VLSI implementation

Cited by 3 publications

References 16 publications

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

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

RETRACTED: Light music online system based on FPGA and Convolutional Neural Network

VLSI implementation of image compressor using probabilistic run length coding

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