scholarly journals Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

2021 ◽  
Vol 11 (2) ◽  
pp. 122-134
Author(s):  
Saleh Alshehri
Keyword(s):  
Neural Networks ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
Discrete Cosine Transformation ◽  
Compression Technique ◽  
Cosine Transform ◽  
Image Measure ◽  
Transform Coefficients ◽  
Execution Times

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.

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

Repetitive Iteration CORDIC based Energy Efficient Image Compression for Portable Devices

2019 ◽  
Vol 26 (1) ◽  
pp. 1-8
Author(s):  
Naveen Cheggoju ◽  
Neha Nawandar ◽  
Vishal Satpute
Keyword(s):  
Image Compression ◽  
Low Power ◽  
Discrete Cosine Transform ◽  
Energy Efficient ◽  
The Other ◽  
Portable Devices ◽  
Compression Technique ◽  
Cosine Transform ◽  
Power Efficient ◽  
Joint Photographic Experts Group

The rapid advancements in technology in recent years have led to a massive increase in the exchange of data (images, videos, audio, etc.) between portable devices. This has invoked the necessity for building algorithms which consume low power with no compromise in the performance. In this paper, the above captioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation, and performs equally well when compared to Joint Photographic Experts Group (JPEG) standard. Results have been obtained via Matrix Laboratory (MATLAB) and they show that the proposed technique performs equally well and consumes less power in comparison with the other techniques.

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.

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