scholarly journals Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC

2012 ◽  
Author(s):  
K. Somasundaram ◽  
S. Vimala
Keyword(s):  
Image Compression ◽  
Coding Efficiency ◽  
Multi Level

scholarly journals Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access

2018 ◽  
Vol 12 (2) ◽  
pp. 765 ◽  
Author(s):  
T Kavitha ◽  
K. Jayasankar
Keyword(s):  
Image Compression ◽  
Smart Phone ◽  
Computation Time ◽  
Digital Data ◽  
Compression Technique ◽  
Lossless Image Compression ◽  
Coding Efficiency ◽  
Average Computation Time ◽  
Ubiquitous Access ◽  
Cloud Storage Environment

<p>Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we consider Lossless image compression technique, minimizing the number of bits for encoding will aid in improving the coding efficiency and high compression. Fixed length coding cannot assure in minimizing bit length. In order to minimize the bits variable Length codes with prefix-free codes nature are preferred. However the existing compression model presented induce high computing overhead, to address this issue, this work presents an ideal and efficient modified Huffman technique that improves compression factor up to 33.44% for Bi-level images and 32.578% for Half-tone Images. The average computation time both encoding and decoding shows an improvement of 20.73% for Bi-level images and 28.71% for Half-tone images. The proposed work has achieved overall 2% increase in coding efficiency, reduced memory usage of 0.435% for Bi-level images and 0.19% for Half-tone Images. The overall result achieved shows that the proposed model can be adopted to support ubiquitous access to digital data.</p>

scholarly journals Image Compression Using Neural Networks: A Review

2021 ◽  
Vol 17 (14) ◽  
pp. 135-153
Author(s):  
Haval Tariq Sadeeq ◽  
Thamer Hassan Hameed ◽  
Abdo Sulaiman Abdi ◽  
Ayman Nashwan Abdulfatah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Compression ◽  
Image Coding ◽  
Image Data ◽  
Coding System ◽  
Memory Space ◽  
Coding Efficiency ◽  
Huge Data ◽  
The Neural Network

Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

Improved Listless Embedded Block Partitioning Algorithms for Image Compression

2014 ◽  
Vol 14 (04) ◽  
pp. 1450020 ◽  
Author(s):  
Ranjan Kumar Senapati ◽  
Prasanth Mankar
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Rate Distortion ◽  
Set Partitioning ◽  
Breadth First Search ◽  
Block Partitioning ◽  
Dynamic Memory ◽  
Coding Efficiency ◽  
Hierarchical Trees ◽  
Block Based

In this paper, two simple yet efficient embedded block-based image compression algorithms are presented. These algorithms not only improve the rate distortion performances of set partitioning in hierarchical trees (SPIHT) and set partitioning in embedded block coder (SPECK) at lower bit rates but also reduces the dynamic memory requirement by 91.1% in comparison to SPIHT. The former objective is achieved by better exploiting the coefficient decaying spectrum of the wavelet transformd images and the later objective is realised by improved listless implementation of the algorithms. The proposed algorithms explicitly perform breadth first search like SPECK. Extensive simulation conducted on various standard grayscale and color images indicate significant peak-signal-to-noise-ratio (PSNR) improvement over most of the state-of-the-art wavelet-based embedded coders including JPEG2000 at lower rates. The reduction of encoding and decoding time as well as improvement in coding efficiency at lower bit rates facilitate these coder as better candidates for multimedia applications.

scholarly journals Dictionary based Image Compression via Sparse Representation

2017 ◽  
Vol 7 (4) ◽  
pp. 1964 ◽  
Author(s):  
Arabinda Sahoo ◽  
Pranati Das
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Sparse Representation ◽  
Data Transmission ◽  
State Of The Art ◽  
Experimental Result ◽  
Storage Space ◽  
Image Block ◽  
Current Image ◽  
Coding Efficiency

Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The over-complete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained over-complete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods.

Multi-level fuzzy transforms image compression

2018 ◽  
Vol 10 (7) ◽  
pp. 2745-2756 ◽  
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Image Compression ◽  
Multi Level
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