Efficient greyscale image compression technique based on vector quantization

2011 ◽  
Vol 19 (1) ◽  
Author(s):  
Y. Hu ◽  
J. Chuang ◽  
C. Lo ◽  
C. Li
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Vector Quantization ◽  
Image Coding ◽  
Reconstructed Image ◽  
Compression Technique ◽  
Image Encoding ◽  
Expansion Technique ◽  
Prediction Technique ◽  
Greyscale Image

AbstractIn this paper, a novel greyscale image coding technique based on vector quantization (VQ) is proposed. In VQ, the reconstructed image quality is restricted by the codebook used in the image encoding/decoding procedures. To provide a better image quality using a fixed-sized codebook, the codebook expansion technique is introduced in the proposed technique. In addition, the block prediction technique and the relatively address technique are employed to cut down the required storage cost of the compressed codes. From the results, it is shown that the proposed technique adaptively provides better image quality at low bit rates than VQ.

Image compression: Rate versus distortion assessment

1984 ◽  
Vol 42 ◽  
pp. 538-541
Author(s):  
William B. Pennebaker ◽  
Cesar A. Gonzales ◽  
Joan L. Mitchell
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Digital Image ◽  
Image Coding ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Compression Rate ◽  
Loss Of Information ◽  
Coding Schemes ◽  
Rate Versus

The aim of digital image coding for compression is to minimize memory for storage and/or bandwidth for transmission. In the case of grayscale images, compression is typically accompanied by some distortion or loss of information in the reconstructed image. Generally, the larger the distortion, the better the compression; a balance between these competing trends has to be achieved in any practical coding design. Different coding schemes can result in distortions which are perceived differently by viewers. These various schemes can also give different compression rates, but rate comparisons among them are hampered by the lack of a good measure of image fidelity consistent with subjective appreciation of image quality by the human viewer. A problem that is simpler, but still useful, is the study of the rate-distortion tradeoff for a particular type of distortion such as that produced by a particular class of coders.

Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

2012 ◽  
Vol 488-489 ◽  
pp. 1587-1591
Author(s):  
Amol G. Baviskar ◽  
S. S. Pawale
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Low Complexity ◽  
Contractive Mapping ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Image Encoding ◽  
Fractal Image ◽  
Coding Method ◽  
The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

2021 ◽  
Author(s):  
T. Satish Kumar ◽  
S. Jothilakshmi ◽  
Batholomew C. James ◽  
M. Prakash ◽  
N. Arulkumar ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Image Compression ◽  
Vector Quantization ◽  
Optimization Techniques ◽  
Smooth Transition ◽  
Local Optimum ◽  
Compression Technique ◽  
Biomedical Images ◽  
Compression Performance ◽  
Biomedical Image

In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde–Buzo–Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.

Image coding using wavelet transforms and vector quantization with error correction

Robotica ◽  
1999 ◽  
Vol 17 (2) ◽  
pp. 219-227
Author(s):  
H. Zenkouar ◽  
A. Nachit
Keyword(s):  
Image Compression ◽  
Visual System ◽  
Error Correction ◽  
Vector Quantization ◽  
Image Coding ◽  
Wavelet Transforms ◽  
Self Organizing Maps ◽  
Frequency Domains ◽  
Self Organizing

Image compression is essential for applications such as transmission of databases, etc. In this paper, we propose a new scheme for image compression combining recursive wavelet transforms with vector quantization. This method is based on the Kohonen Self-Organizing Maps (SOM) which take into account features of a visual system in both space and frequency domains.

ITERATION-FREE FRACTAL CODING FOR IMAGE COMPRESSION USING GENETIC ALGORITHM

2008 ◽  
Vol 07 (04) ◽  
pp. 429-446 ◽  
Author(s):  
A. R. NADIRA BANU KAMAL ◽  
S. THAMARAI SELVI ◽  
HENRY SELVARAJ
Keyword(s):  
Genetic Algorithm ◽  
Image Quality ◽  
Image Compression ◽  
Image Coding ◽  
Computing Time ◽  
Search Space ◽  
Process Time ◽  
Fractal Coding ◽  
Quad Tree ◽  
Single Block

An iteration-free fractal coding for image compression is proposed using genetic algorithm (GA) with elitist model. The proposed methodology reduces the coding process time by minimizing intensive computations. The proposed technique utilizes the GA, which greatly decreases the search space for finding the self-similarities in the given image. The performance of the proposed method is compared with the iteration-free fractal-based image coding using vector quantization method for both single block and Quad tree partition on benchmark images for parameters such as image quality and coding time. It is observed that the proposed method achieves excellent performance in image quality with reduction in computing time.

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