scholarly journals Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram

2021 ◽  
Vol 38 (4) ◽  
pp. 993-1006
Author(s):  
Vimala Kumari Gollu ◽  
Ganta Usha Sravani ◽  
Mandru Sunil Prakash ◽  
Ganta Srikanth
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Optimization Techniques ◽  
Multilevel Thresholding ◽  
Mr Images ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
And Storage ◽  
2D Histogram

In recent times, medical scan images are crucial for accurate diagnosis by medical professionals. Due to the increasing size of the medical images, transfer and storage of images require huge bandwidth and storage space, and hence needs compression. In this paper, multilevel thresholding using 2-D histogram is proposed for compressing the images. In the proposed work, hybridization of optimization techniques viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS) is used to optimize the multilevel thresholding process by assuming the Renyi entropy as an objective function. Meaningful clusters are possible with optimal threshold values, which lead to better image compression. For performance evaluation, the proposed work has been examined on six Magnetic Resonance (MR) images of brain and compared with individual optimization techniques as well as with 1-D histogram. Recent study reveals that peak signal to noise ratio (PSNR) fail in measuring the visual quality of reconstructed image because of mismatch with the objective mean opinion scores (MOS). So, we incorporate weighted PSNR (WPSNR) and visual PSNR (VPSNR) as performance measuring parameters of the proposed method. Experimental results reveal that hGAPSO-SOS method can be accurately and efficiently used in problem of multilevel thresholding for image compression.

scholarly journals Novel 2-D histogram based Soft thresholding for Brain Tumor Detection and Image Compression

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Image Compression ◽  
Hybrid Genetic Algorithm ◽  
Absolute Threshold ◽  
Visual Quality ◽  
Magnetic Resonance Images ◽  
Optimization Techniques ◽  
Multilevel Thresholding ◽  
Equal Distribution ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

The objective of image compression is to extract meaningful clusters in a given image. Significant groups are possible with absolute threshold values. 1-D histogram-based multilevel thresholding is computationally complex and reconstructed image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. So, 2-D histogram-based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel hybrid Genetic Algorithm, Particle Swarm Optimization and Symbiotic Organisms Search (hGAPSO-SOS), and the obtained results are compared with state of the art optimization techniques. Recent study reveals that PSNR fails in measuring the visual quality because of mismatch with the objective mean opinion scores (MOS). So, we incorporate a weighted PSNR (WPSNR) and visual PSNR (VPSNR). Experimental results examined on Magnetic Resonance images of brain, and results with 2-D histogram reveal that hGAPSO-SOS method can be efficiently and accurately used in multilevel thresholding problem.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

Comparative Performance Analysis of Optimization Techniques on Vector Quantization for Image Compression

2017 ◽  
Vol 7 (1) ◽  
pp. 19-43 ◽  
Author(s):  
Karri Chiranjeevi ◽  
Umaranjan Jena ◽  
Sonali Dash
Keyword(s):  
Performance Analysis ◽  
Image Compression ◽  
Vector Quantization ◽  
Signal To Noise Ratio ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Optimization Techniques ◽  
Convergence Time ◽  
Comparative Performance

Linde-Buzo-Gray (LBG) Vector Quantization (VQ), technically generates local codebook after many runs on different sets of training images for image compression. The key role of VQ is to generate global codebook. In this paper, we present comparative performance analysis of different optimization techniques. Firefly and Cuckoo search generate a near global codebook, but undergoes problem when non-availability of brighter fireflies and convergence time is very high respectively. Hybrid Cuckoo Search (HCS) algorithm was developed and tested on four benchmark functions, that optimizes the LBG codebook with less convergence rate by taking McCulloch's algorithm based levy flight and variant of searching parameters. Practically, we observed that Bat algorithm (BA) peak signal to noise ratio is better than LBG, FA, CS and HCS in between 8 to 256 codebook sizes. The convergence time of BA is 2.4452, 2.734 and 1.5126 times faster than HCS, CS and FA respectively.

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

2020 ◽  
Vol 11 (2) ◽  
pp. 31-61
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Symbiotic Relationship ◽  
Symbiotic Organisms Search ◽  
Image Thresholding ◽  
Prime Concern ◽  
Feature Similarity Index ◽  
Symbiotic Organisms

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

A NONUNIFORM HIGH-QUALITY IMAGE COMPRESSION METHOD TO PRESERVE USER-SPECIFIED COMPRESSION RATIO

2011 ◽  
Vol 11 (03) ◽  
pp. 355-375 ◽  
Author(s):  
MOHAMMAD REZA BONYADI ◽  
MOHSEN EBRAHIMI MOGHADDAM
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Linear Mapping ◽  
Compression Method ◽  
High Quality ◽  
Compressed Images ◽  
High Quality Image ◽  
Quality Image

Most of image compression methods are based on frequency domain transforms that are followed by a quantization and rounding approach to discard some coefficients. It is obvious that the quality of compressed images highly depends on the manner of discarding these coefficients. However, finding a good balance between image quality and compression ratio is an important issue in such manners. In this paper, a new lossy compression method called linear mapping image compression (LMIC) is proposed to compress images with high quality while the user-specified compression ratio is satisfied. This method is based on discrete cosine transform (DCT) and an adaptive zonal mask. The proposed method divides image to equal size blocks and the structure of zonal mask for each block is determined independently by considering its gray-level distance (GLD). The experimental results showed that the presented method had higher pick signal to noise ratio (PSNR) in comparison with some related works in a specified compression ratio. In addition, the results were comparable with JPEG2000.

scholarly journals Improved JPEG Coding by Filtering 8 × 8 DCT Blocks

2021 ◽  
Vol 7 (7) ◽  
pp. 117
Author(s):  
Yasir Iqbal ◽  
Oh-Jin Kwon
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Block Code ◽  
Entropy Coding ◽  
File Size ◽  
Image Encoding ◽  
Coding Standards ◽  
And Storage

The JPEG format, consisting of a set of image compression techniques, is one of the most commonly used image coding standards for both lossy and lossless image encoding. In this format, various techniques are used to improve image transmission and storage. In the final step of lossy image coding, JPEG uses either arithmetic or Huffman entropy coding modes to further compress data processed by lossy compression. Both modes encode all the 8 × 8 DCT blocks without filtering empty ones. An end-of-block marker is coded for empty blocks, and these empty blocks cause an unnecessary increase in file size when they are stored with the rest of the data. In this paper, we propose a modified version of the JPEG entropy coding. In the proposed version, instead of storing an end-of-block code for empty blocks with the rest of the data, we store their location in a separate buffer and then compress the buffer with an efficient lossless method to achieve a higher compression ratio. The size of the additional buffer, which keeps the information of location for the empty and non-empty blocks, was considered during the calculation of bits per pixel for the test images. In image compression, peak signal-to-noise ratio versus bits per pixel has been a major measure for evaluating the coding performance. Experimental results indicate that the proposed modified algorithm achieves lower bits per pixel while retaining quality.

scholarly journals Finding Radial Network Configuration of Distribution System Based on Modified Symbiotic Organisms Search

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Thuan Thanh Nguyen ◽  
Thanh-Quyen Ngo ◽  
Thanh Long Duong ◽  
Thang Trung Nguyen
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Network Reconfiguration ◽  
Success Rates ◽  
Network Configuration ◽  
Symbiotic Organisms Search ◽  
Comparison Results ◽  
Symbiotic Organisms ◽  
Radial Network

Network reconfiguration (NR) is one of the most effective methods to reduce line power loss in the distribution system, which causes higher losses than the other parts of the power system. This paper proposes a modified symbiotic organisms search (MSOS) algorithm to solve the NR problem. For the purpose of enhancing the effectiveness of MSOS, the mutualism and parasitism strategies of the original symbiotic organisms search (SOS) have been modified to create better new solutions. In the mutualism strategy, the so-far best solution is updated immediately as soon as new solutions are created. In the parasitism strategy, the update is only implemented for the first half of control variables, whereas another half still remains unchanged. The comparison results between MSOS and SOS on twenty-five benchmark functions and different scales of test distribution systems with 14, 33, 69, and 119 nodes show that the improvement level of MSOS over SOS is significant with higher success rates and better quality of gained solutions. Similarly, MSOS also reaches better results than other methods in the literature. Consequently, MSOS can be a favorable method for determining the most appropriate configurations for the distribution systems.

scholarly journals Chaotic immune symbiotic organisms search for SVC installation in voltage security control

2019 ◽  
Vol 16 (2) ◽  
pp. 623
Author(s):  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Ismail Musirin ◽  
Saiful Izwan Suliman ◽  
Muhammad Murtadha Othman
Keyword(s):  
Power System ◽  
Energy Demand ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Symbiotic Organisms Search ◽  
Voltage Security ◽  
Facts Devices ◽  
Security Control ◽  
The World ◽  
Symbiotic Organisms

<span>Parallel with the urbanization of the world, energy demand in the world also increased. The increase in energy demand will require a power system to be operated near its stability limit. To mitigate the problem, Flexible Alternating Current Transmission System (FACTS) devices can be installed as a compensation scheme to improve voltage security in a power system. For an effective compensation, FACTS devices should be optimally allocated in a power system. Although optimization techniques can be implemented to optimally allocate these devices, problems have been reported which would affect the performance of the optimization techniques in terms of producing high quality solutions. This paper presents the implementation of Chaotic Immune Symbiotic Organisms Search for solving optimal Static VAr Compensator (SVC) allocation problem for voltage security control. The optimization is validated in IEEE 26-Bus Reliability Test System (RTS) realizes the capability of CISOS in solving the optimization problem. Comparative studies with respect to Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) resulting in good agreement on the results and demonstrated superior performance of CISOS. Results of the study can be beneficial to power system community in terms of compensation planning prior to real world implementation.</span>

Vector Quantization for Satellite Image Compression

2016 ◽  
Vol 5 ◽  
pp. 22
Author(s):  
Sanjith Sathya Joseph ◽  
R. Ganesan
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Signal To Noise ◽  
Transmission Process ◽  
Noise Ratio ◽  
Time Required

Image compression is the process of reducing the size of a file without humiliating the quality of the image to an unacceptable level by Human Visual System. The reduction in file size allows as to store more data in less memory and speed up the transmission process in low bandwidth also, in case of satellite images it reduces the time required for the image to reach the ground station. In order to increase the transmission process compression plays an important role in remote sensing images.  This paper presents a coding scheme for satellite images using Vector Quantization. And it is a well-known technique for signal compression, and it is also the generalization of the scalar quantization.  The given satellite image is compressed using VCDemo software by creating codebooks for vector quantization and the quality of the compressed and decompressed image is compared by the Mean Square Error, Signal to Noise Ratio, Peak Signal to Noise Ratio values.

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