Optimized active contor segmentation model for medical image compression

Tamboli, Shabanam Shabbir; Butta, Rajasekhar; Jadhav, T. Sharad; Bhatt, Abhishek

doi:10.1016/j.bspc.2022.104244

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…The traditional method to reduce the bandwidth is compression, which is also reported in recent literature [15][16][17]. The main goal of the compression technique is to eliminate the redundancy of image data so that the digital image will be stored and transmitted more effectively.…”

Section: Introductionmentioning

confidence: 99%

“…Joint source-channel coding is the encoding of a redundant information source for transmission over a noisy channel and the corresponding decoding, using a single code instead of the more conventional steps of source coding followed by channel coding. Deep learning has also been extended to JSCC systems for wireless image transmission to perform better than separated source and channel coding [17,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Computationally efficient low-power sigma delta modulation-based image processing algorithm

Pathan,

Memon,

Memon

et al. 2023

Mehran Univ. res. j. eng. technol.

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Digital Image Processing has dominated Digital Signal Processing at the cost of more memory, resources, and high computational power. In image processing, filtering transformations and other operations need complex multiplications, and the multiplier is one of the most resources consuming elements. Recently, mitigating the multiplier complexity in the digital signal processing (DSP) algorithms sigma-delta modulation based general purpose and adaptive DSP algorithms are developed in MATLAB and compared with its counterpart multi-bit algorithms for functionality and area-performance-power in FPGA. The contemporary multiplier algorithms are also optimized to overcome the multiplier complexity challenge as computation becomes simple and fast. This paper extends the reported work by investigating the sigma-delta modulation approaches for developing a computationally efficient low-power image processing algorithm. The proposed model is designed, developed, and simulated in MATLAB. The simulation results are analyzed using SNR, MSE, and Peak SNR. The simulation results show that the proposed system can better mitigate the noise effect, making it robust for noisy environment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computationally efficient low-power sigma delta modulation-based image processing algorithm

Pathan,

Memon,

Memon

et al. 2023

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

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Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi

2023

Soft Comput

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Image compression is one of the most attractive and practical topics. Without image compression, the image size becomes too large for storage. The transmission of uncompressing images on computer networks slows down and network bandwidth is wasted. Various approaches to image compression have been proposed so far, one of which is vector quantization using mathematical concepts and image processing to compress images. The LBG algorithm is a practical algorithm for compressing images using vector quantization concepts. Most researchers have used metaheuristic and optimization algorithms with a modeling approach of swarm behavior of living things to improve the quality of the LBG compression algorithm. This study uses a meta-heuristic method based on sine and cosine algorithms (SCA) to improve the quality of the image compression algorithm. In the proposed mathematical modeling method, the SCA algorithm is improved using spiral equations. The improved SCA algorithm is then used to find the optimal codebook in the LBG compression algorithm. Finding a better codebook in the proposed method will increase the quality of the compression images. The proposed method implemented in MATLAB software and experiments showed that the PSNR index in the proposed method improve the ratio of the LBG algorithm by about 13.73%. Evaluations show that the PSNR index of compressed images in the proposed method is higher and better than PBM, CS-LBG, FA-LBG, BA-LBG, HBMO-LBG, QPSO-LBG, PSO-LBG. The result shows the proposed method (or ISCA-LBG) has less time complexity than HHO and WOA compression algorithms.

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