scholarly journals Robust Image Compression Algorithm using Discrete Fractional Cosine Transform

2022 ◽  
Vol 17 ◽  
pp. 25-33
Author(s):  
Vivek Arya
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Fractional Fourier Transform ◽  
Signal To Noise Ratio ◽  
Quality Parameters ◽  
Cosine Transform ◽  
Time Frequency ◽  
Compressed Images ◽  
Discrete Fractional Fourier Transform ◽  
Image Compression Algorithm

The discrete fractional Fourier transform become paradigm in signal processing. This transform process the signal in joint time-frequency domain. The attractive and very important feature of DFrCT is an availability of extra degree of one free parameter that is provided by fractional orders and due to which optimization is possible. Less execution time and easy implementation are main advantages of proposed algorithm. The merit of effectiveness of proposed technique over existing technique is superior due to application of discrete fractional cosine transform by which higher compression ratio and PSNR are obtained without any artifacts in compressed images. The novelty of the proposed algorithm is no artifacts in compressed image along with good CR and PSNR. Compression ratio (CR) and peak signal to noise ratio (PSNR) are quality parameters for image compression with optimum fractional order.

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 Image Compression System using ANN

2012 ◽  
pp. 20-24
Author(s):  
Disha Parkhi ◽  
S. S. Lokhande
Keyword(s):  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
High Definition ◽  
Still Images ◽  
Cosine Transform ◽  
Compression System ◽  
High Definition Television ◽  
Compressed Images ◽  
The Neural Network

The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and high definition television (HDTV) has increased the need for effective and standardized image compression techniques. Among the emerging standards are JPEG, for compression of still images; MPEG, for compression of motion video; and CCITT H.261 (also known as Px64), for compression of video telephony and teleconferencing. All three of these standards employ a basic technique known as the discrete cosine transform (DCT), Developed by Ahmed, Natarajan, and Rao [1974]. Image compression using Discrete Cosine Transform (DCT) is one of the simplest commonly used compression methods. The quality of compressed images, however, is marginally reduced at higher compression ratios due to the lossy nature of DCT compression, thus, the need for finding an optimum DCT compression ratio. An ideal image compression system must yield high quality compressed images with good compression ratio, while maintaining minimum time cost. The neural network associates the image intensity with its compression ratios in search for an optimum ratio.

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.

Learning Aided Digital Image Compression Technique for Medical Application

2016 ◽  
pp. 400-422
Author(s):  
Kandarpa Kumar Sarma
Keyword(s):  
Image Compression ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Medical Application ◽  
Learning Systems ◽  
Signal To Noise ◽  
Compression Technique ◽  
Compressed Images ◽  
Digital Image Compression ◽  
Efficiency And Reliability

The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.

Image Compression Algorithm with Radix-4 FFT

2010 ◽  
Vol 143-144 ◽  
pp. 404-408
Author(s):  
Jia Bin Deng ◽  
Juan Li Hu ◽  
He Hua Chi ◽  
Jue Bo Wu
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Image Compression ◽  
Compression Ratio ◽  
Conformal Symmetry ◽  
Research Focus ◽  
Data Store ◽  
Long Time ◽  
Image Compression Algorithm

Image compression technology has been the research focus in the field of image processing all the time. In this paper, Radix-4 FFT is introduced to realize limit distortion coding of image. The presented method aims to solve the problems of Fourier transform on existing complexity and long time-consuming, and it can reduce the number of data store by conformal symmetry of Fourier transform. Using Radix-4 FFT, the time-consuming can be highly shortened and two different kinds of quantization tables are designed according to image compression ratio and the quality of image.

scholarly journals Image Compression Based on Arithmetic Coding Algorithm

2021 ◽  
pp. 329-334
Author(s):  
Ruaa Ibrahim Yousif ◽  
Nassir Hussein Salman
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Rapid Development ◽  
Arithmetic Coding ◽  
Compact Form ◽  
Color Model ◽  
Cosine Transform ◽  
High Image Quality ◽  
The Past

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.

scholarly journals DESIGN OF NEURAL NETWORK AS DATA FLOW MODEL FOR IMAGE COMPRESSION

2013 ◽  
pp. 195-199
Author(s):  
DANESHWARI I. HATTI ◽  
SAVITRI RAJU ◽  
MAHENDRA M. DIXIT
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
Convergence Time ◽  
Training Algorithms ◽  
Hardware Complexity ◽  
Cosine Transform ◽  
Hidden Neurons

In digital communication bandwidth is essential parameter to be considered. Transmission and storage of images requires lot of memory in order to use bandwidth efficiently neural network and Discrete cosine transform together are used in this paper to compress images. Artificial neural network gives fixed compression ratio for any images results in fixed usage of memory and bandwidth. In this paper multi-layer feedforward neural network has been employed to achieve image compression. The proposed technique divides the original image in to several blocks and applies Discrete Cosine Transform (DCT) to these blocks as a pre-process technique. Quality of image is noticed with change in training algorithms, convergence time to attain desired mean square error. Compression ratio and PSNR in dB is calculated by varying hidden neurons. The proposed work is designed using MATLAB 7.10. and synthesized by mapping on Vertex 5 in Xilinx ISE for understanding hardware complexity. Keywords - backpropagation, Discrete

Embedded Image Compression Algorithm and FPGA Implementation Based on BP Neural Networks

2011 ◽  
Vol 65 ◽  
pp. 415-418
Author(s):  
Guang Ming Li ◽  
Zhen Qi He
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Compression ◽  
Compression Ratio ◽  
Bp Neural Network ◽  
Stable Operation ◽  
High Compression Ratio ◽  
The Neural Network ◽  
Embedded Image ◽  
Image Compression Algorithm

At present, because more embedded image compressions are single, various compression methods have not transplant to embedded equipment. In this paper, A BP neural network based image compression methods have been proposed. The neural network is trained more and more, and obtained a set of weights and thresholds usefully. Then, use the FPGA to achieve, In the FPGA, using the framework of soft-core Nios Ⅱ way. Ultimately, compression program written using Verilog and burned into the FPGA. Experiments show that the system has the advantages of high compression ratio, small size, and can stable operation.

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