scholarly journals DESIGN OF NEURO-WAVELET BASED VECTOR QUANTIZER FOR IMAGE COMPRESSION

2011 ◽  
pp. 50-57
Author(s):  
Rasmita Lenka ◽  
Swagatika Padhi ◽  
Minakshee Behera ◽  
Naresh Patnaik ◽  
Mihir N. Mohanty
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Image Data ◽  
Vector Spaces ◽  
Storage Space ◽  
Higher Dimension ◽  
Dimension Vector ◽  
Vector Quantizer ◽  
Novel Approach ◽  
Minimum Distortion

This paper presents a novel approach to design a vector quantizer for image compression. Compression of image data using Vector Quantization (VQ) will compare Training Vectors with Codebook that has been designed. The result is an index of position with minimum distortion. Moreover it provides a means of decomposition of the signal in an approach which takes the improvement of inter and intra band correlation as more lithe partition for higher dimension vector spaces. Thus, the image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. In addition the proposed paper also presents a survey on different methods of vector quantization for image compression and application of SOFM.

scholarly journals An Intelligent Method Based Medical Image Compression

2013 ◽  
pp. 1-6
Author(s):  
Jibanananda Mishra ◽  
Soumya Ranjan Parida ◽  
Mihir N. Mohanty ◽  
Ranjan Kumar Jena
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Medical Image ◽  
Data Sets ◽  
Storage Space ◽  
Vector Quantizer ◽  
Large Sets ◽  
High Quality Image ◽  
Quality Image ◽  
And Storage

Compression methods are important in many medical applications to ensure fast interactivity through large sets of images (e.g. volumetric data sets, image databases), for searching context dependant images and for quantitative analysis of measured data. Medical data are increasingly represented in digital form. The limitations in transmission bandwidth and storage space on one side and the growing size of image datasets on the other side has necessitated the need for efficient methods and tools for implementation. Many techniques for achieving data compression have been introduced. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for Teleradiology. This paper presents an approach of intelligent method to design a vector quantizer for image compression. The image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. The proposed approach presents an efficient method of vector quantization for image compression and application of SOFM.

A Review of Contemporary Image Compression Techniques and Standards

2020 ◽  
pp. 121-157 ◽  
Author(s):  
Tawheed Jan Shah ◽  
M. Tariq Banday
Keyword(s):  
Image Compression ◽  
Discrete Cosine Transform ◽  
Image Data ◽  
Multimedia Data ◽  
Transmission Time ◽  
Storage Space ◽  
Cosine Transform ◽  
Processing Power ◽  
Space Processing ◽  
Time Required

Uncompressed multimedia data such as images require huge storage space, processing power, transmission time, and bandwidth. In order to reduce the storage space, transmission time, and bandwidth, the uncompressed image data is compressed before its storage or transmission. This process not only permits a large number of images to be stored in a specified amount of storage space but also reduces the time required for them to be sent or download from the internet. In this chapter, the classification of an image on the basis of number of bits used to represent each pixel of the digital image and different types of image redundancies is presented. This chapter also introduced image compression and its classification into different lossless and lossy compression techniques along with their advantages and disadvantages. Further, discrete cosine transform, its properties, and the application of discrete cosine transform-based image compression method (i.e., JPEG compression model) along with its limitations are also discussed in detail.

Image Compression Using Vector Quantization with Variable Block Size Division

2010 ◽  
Vol 130 (8) ◽  
pp. 1431-1439 ◽  
Author(s):  
Hiroki Matsumoto ◽  
Fumito Kichikawa ◽  
Kazuya Sasazaki ◽  
Junji Maeda ◽  
Yukinori Suzuki
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Block Size ◽  
Variable Block

Stable and Supported Semantics in Continuous Vector Spaces

2020 ◽  
Author(s):  
Yaniv Aspis ◽  
Krysia Broda ◽  
Alessandra Russo ◽  
Jorge Lobo
Keyword(s):  
Logic Program ◽  
Matrix Multiplication ◽  
Vector Spaces ◽  
Continuous Space ◽  
Continuous Vector ◽  
Novel Approach ◽  
Gradient Based ◽  
Normal Logic ◽  
Parameter Values ◽  
Normal Logic Program

We introduce a novel approach for the computation of stable and supported models of normal logic programs in continuous vector spaces by a gradient-based search method. Specifically, the application of the immediate consequence operator of a program reduct can be computed in a vector space. To do this, Herbrand interpretations of a propositional program are embedded as 0-1 vectors in $\mathbb{R}^N$ and program reducts are represented as matrices in $\mathbb{R}^{N \times N}$. Using these representations we prove that the underlying semantics of a normal logic program is captured through matrix multiplication and a differentiable operation. As supported and stable models of a normal logic program can now be seen as fixed points in a continuous space, non-monotonic deduction can be performed using an optimisation process such as Newton's method. We report the results of several experiments using synthetically generated programs that demonstrate the feasibility of the approach and highlight how different parameter values can affect the behaviour of the system.

Differential Image Compression for Telemedicine: A Novel Approach

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Adnan Alam Khan ◽  
Dr. Asadullah Shah ◽  
Saghir Muhammad
Keyword(s):  
Image Compression ◽  
Medical Information ◽  
Emerging Technologies ◽  
Medical Images ◽  
Image Transmission ◽  
Medical Doctors ◽  
Medical Sciences ◽  
Compression Method ◽  
Paramedical Staff ◽  
Novel Approach

Telemedicine is one of the most emerging technologies of applied medical sciences. Medical information related to patients is transmitted and stored for references and consultations. Medical images occupy huge space; in order to transmit these images may delay the process of image transmission in critical times. Image compression techniques provide a better solution to combat bandwidth scarcity problems, and transmit same image in a much lower bandwidth requirements, more faster and at the same time maintain quality. In this paper a differential image compression method is developed in which medical images are taken from a wounded patient and are compressed to reduce the bit rate of these images. Results indicate that on average 25% compression on images is achieved with 3.5 MOS taken from medical doctors and other paramedical staff the ultimately user of the images.

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