Approach For Image Compression Research Articles

An improved lossless image compression based arithmetic coding using mixture of non-parametric distributions

In this paper, we propose a new approach for a block-based lossless image compression using finite mixture models and adaptive arithmetic coding. Conventional arithmetic encoders encode and decode images sample-by-sample in raster scan order. In addition, conventional arithmetic coding models provide the probability distribution for whole source symbols to be compressed or transmitted, including static and adaptive models. However, in the proposed scheme, an image is divided into non-overlapping blocks and then each block is encoded separately by using arithmetic coding. The proposed model provides a probability distribution for each block which is modeled by a mixture of non-parametric distributions by exploiting the high correlation between neighboring blocks. The Expectation-Maximization algorithm is used to find the maximum likelihood mixture parameters in order to maximize the arithmetic coding compression efficiency. The results of comparative experiments show that we provide significant improvements over the state-of-the-art lossless image compression standards and algorithms. In addition, experimental results show that the proposed compression algorithm beats JPEG-LS by 9.7 % when switching between pixel and prediction error domains.

Intelligent fuzzy approach for fast fractal image compression

Abstract Fractal image compression (FIC) is recognized as a NP-hard problem, and it suffers from a high number of mean square error (MSE) computations. In this paper, a two-phase algorithm was proposed to reduce the MSE computation of FIC. In the first phase, based on edge property, range and domains are arranged. In the second one, imperialist competitive algorithm (ICA) is used according to the classified blocks. For maintaining the quality of the retrieved image and accelerating algorithm operation, we divided the solutions into two groups: developed countries and undeveloped countries. Simulations were carried out to evaluate the performance of the developed approach. Promising results thus achieved exhibit performance better than genetic algorithm (GA)-based and Full-search algorithms in terms of decreasing the number of MSE computations. The number of MSE computations was reduced by the proposed algorithm for 463 times faster compared to the Full-search algorithm, although the retrieved image quality did not have a considerable change.

Complex‐valued interferometric inverse synthetic aperture radar image compression base on compressed sensing

Complex-valued interferometric inverse synthetic aperture radar (InISAR) image compression is discussed in this study. The target scene has its continuity and is compressible. However, because of the random phase of each resolution cell, the frequency spectrum of an ISAR image is wide and the complex-valued image is hard to compress. A complex-valued ISAR image compression approach is proposed. Using two or more antennas and interferometry processing, the random phase of image pixel can be cancelled and the frequency spectrum becomes sparse. Therefore the theory of compressed sensing can be introduced to the process of the complex-valued image compression. Hence, the complex-valued InISAR image compression and reconstruction can be completed. Results on real data are presented to validate the method. In comparison with results of the conventional compression techniques, the proposed method shows the better ability to preserve both the imaging magnitude and interferometric phase.

An Effective Selection of DCT and DWT Coefficients for an Adaptive Medical Image Compression Technique Using Multiple Kernel FCM

Over the last two decades, great developments have been made in image compression approaches driven by a growing demand for storage and transmission of visual information. However, a number of publications have demonstrated good results of the compressed domain approach to image analysis. Nevertheless, very little work has been carried out on ROI based compression. In this paper, we have proposed an adaptive approach to compress the image without lossless version using selection of DCT and DWT coefficients and MKFCM. Our proposed approach consists of three stages, (i) Region segmentation (ii) Image compression (iii) Image decompression. At first, the input medical image is segmented by ROI, Non-ROI and background using MKFCM. Subsequently, the ROI region compressed by DCT and SPIHT coding and Non-ROI region is compressed by DWT and Huffman coding. The non-relevant regions are directly converted to zero. From the compressed regions like as ROI, Non-ROI and background, finally we obtain compression ratio to evaluate the proposed approach. Then, in decompression stage, the original medical image is extracted using the devised procedure. We can also see that our proposed image compression approach have outperformed by having better compression ratio of 4.6446 when compared existing technique only achieved 4.4326

Improved Bandelet with heuristic evolutionary optimization for image compression

As an efficient geometric analysis tool, Bandelet has exhibited enormous potential in image compression for its capability in capturing the geometrical structure in images. However, in available implementation of Bandelet, the optimal geometric flows are determined by a coarse and exhaustive search, which will degrade the performance of Bandelet in an accurate representation of images. In this paper, we advance a new image compression approach based on Laplacian Pyramid (LP) and improved Bandelet, where a Heuristic Memetic Algorithm (HMA) is proposed to locate accurate geometric flows. The chromosome is defined to represent geometric flows, and local and heuristic evolution operators are employed to make fast search possible. The improved Bandelet is used to compress the high-frequency band of the image decomposed by LP, and the low-frequency band is coded by Set Partitioning In Hierarchical Trees (SPIHT) algorithm. Some experiments are taken on some natural images and remote sensing images, and the results demonstrate that our proposed scheme outperforms SPIHT and the second generation Bandelet(2 G-Bandelet) in both PSNR and time consumption at low bit rate compression.

The effect of image compression on synthetic PROBA-V images

We have carried out an in-depth investigation into the effects of image compression on synthetic Probe for On-Board Autonomy – Vegetation (PROBA-V) scenes and Landsat-derived image tiles. The two image compression implementations used were the TER implementation and a bespoke implementation of the Consultative Committee for Space Data Systems (CCSDS) Blue Book standard, which are functionally identical but operate on different image architectures. This work included (1) the development of an approach for producing synthetic scenes that were appropriate in terms of structure and content, and (2) evaluation of the image compression approach on the two kinds of image in terms of their usefulness for land-cover mapping. The synthetic image (SI) generation approach has been rigorously tested and produces images that are statistically similar to real scenes, both compressed and uncompressed. The results of our work show that the effects of image compression vary significantly between bands and with different compression ratios, and that the impact of image compression on image quality does vary with spatial scale. We also found indications of increased error rate at boundaries within the imagery. While the SI generation process and the processing chain of this imagery are not completely consistent with PROBA-V imagery, agreement was found among many of the results produced by the two approaches.

Neural Network Based Image Compression Approach to Improve the Quality of Biomedical Image for Telemedicine

Neural Network Based Image Compression Approach to Improve the Quality of Biomedical Image for Telemedicine

Minimize the Percentage of Noise in Biomedical Images Using Neural Networks

The overall goal of the research is to improve the quality of biomedical image for telemedicine with minimum percentages of noise in the retrieved image and to take less computation time. The novelty of this technique lies in the implementation of spectral coding for biomedical images using neural networks in order to accomplish the above objectives. This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia. We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR), and computation time. To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images. This work explores and tests biomedical images such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).

A Novel Threshold Approach for Medical Image Compression

A Novel Threshold Approach for Medical Image Compression

A Combined Fractal and Wavelet Angiography Image Compression Approach

In this paper, a combined Fractal and Wavelet (CFW) compression algorithm targeting x-ray angiogram images is proposed. Initially, the image is decomposed using wavelet transform. The smoothness of the low frequency part of the image appears as an approximation image with higher self similarities, therefore, it is coded using a fractal coding technique. However, the rest of the image is coded using an adaptive wavelet thresholding technique. This model is implemented and its performance is compared with best performances of the available published algorithms. A data set containing 1000 x-ray angiograms is used to study the performance of the algorithm. A minimum compression ratio of 30 with a peak signal to noise ratio (PSNR) of 36 dB and percent diameter stenosis deviation of (<0.2%) was achieved. Results demonstrate the effectiveness of the proposed technique in obtaining a diagnostic quality of reconstructed images at very low bit rates.

A new arithmetic coding model for a block-based lossless image compression based on exploiting inter-block correlation

In this paper, we investigate a new approach for a block-based lossless image compression using arithmetic coding. The conventional arithmetic encoders encode and decode images pixel by pixel in raster scan order by using a statistical model which provides probabilities for the whole source symbols to be encoded. However, in the proposed scheme, the arithmetic encoders encode an image block by block from left to right, and block-row by block-row from top to bottom. The proposed model estimates the probability distribution of each block by exploiting the high correlation between neighboring image blocks. Therefore, the probability distribution of each block of pixels is estimated by minimizing the Kullback–Leibler distance between the exact probability distribution of that block and the probability distributions of its neighboring blocks in causal order. The results of comparative experiments show significant improvements over conventional arithmetic encoders in both static and adaptive order-0 models, reducing the bitrate by an average of 15.5 and 16.4 % respectively.

Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images

Image compression is applied to many fields such as television dissemination, remote sensing, image storage. Digitized images are compressed by a method which exploits the redundancy of the images so that the number of bits required to represent the image can be reduced with acceptable degradation of the decoded image. The humiliation of the image quality is limited with respect to the application used. There are various biomedical applications where accuracy is of major concern. To attain the objective of performance improvement with respect to decoded picture quality and compression ratios, in contrast to existing image compression techniques, an effective image coding technique which involves transforming the image into another domain with ridgelet function and then quantizing the coefficients with hybrid neural networks combining two different learning networks called auto-associative multilayer perceptron and self-organizing feature map is proposed. Ridge functions are effective in representing functions that have discontinuities along straight lines. Normal wavelet transforms not succeed to represent such functions effectively. The results obtained from the combination of finite ridgelet transform with hybrid neural networks found much better than that obtained from the JPEG2000 image compression system.

MULTIPLE COMPONENT PREDICTIVE CODING OF IMAGES

The conventional multiple component image compression approach separates the input image into several components, each of which is predicted and encoded independently. This approach creates redundancy because the prediction methods as well as the residual subcomponents must be transmitted. In this paper, we propose a new multiple-component predictive coding framework. First, we separate the reconstructed image into several subcomponents. Then, we use the previously encoded subcomponent to predict the current block, and then combine the prediction residuals of each subcomponent. To separate an image into multiple subcomponents, we designed a fast operator-based image separation algorithm. The numerical results demonstrate that the algorithm outperforms the H.264/AVC intra-frame prediction algorithm and the JPEG2000 algorithm on images with ample textures.

Wave atoms based compression method for fingerprint images

This paper proposes a new fingerprint image compression approach where the quality of the decoded image is perceptually controlled using wave atom transform. A comparative study of different transforms shows that wave atom transform is more appropriate than wavelets for fingerprint image compression. A new image quality metric based on the same transform is proposed to control the compression performance. Some properties of the human visual system are exploited in the design of the proposed image quality metric. Simulations show that this metric correlates well with the subjective human judgment. Based on these interesting results a compression method with a perceptual distortion control mechanism specific to fingerprint images is developed. Finally, the performance of the proposed compression scheme is evaluated in terms of fingerprint recognition rate. The experimental results clearly show that the proposed strategy offers better results than some traditional compression methods.

Extrema points coding based on empirical mode decomposition: An improved image sub-band coding method

The extrema points coding approach is an attractive way to represent the image. This approach will potentially become a new scheme to compress the still image and will make a great progress for mobile visual search technique. According to the idea of extrema points coding, a complete image compression approach is presented in this paper. Based on the Empirical Mode Decomposition (EMD) theory, digital image is decomposed into different sub-bands, where the most important extrema points are extracted. On mid and low frequency sub-bands, a novel compression method with obvious advantages over current methods is brought out. Combined this method with Linderhed’s “coding of the EMD using DCT of variable sampled blocks (VSDCTEMD)”, a complete digital image compression scheme is designed. It improves Linderhed’s image compression scheme based on EMD.

A Wavelet Based Approach for Compression of Color Images

The use of color in image analysis and compression is becoming more and more popular. The high quality color images are in demand, but the bandwidth and power resources are limited, this shows the requirement of effective color image compression algorithm which is suitable to human visual system. However most of the existing algorithms are designed for gray scale visual information. In this work a unique wavelet based approach is proposed for compression of color images. Wavelet families are used to characterize the quality of image by calculating quality estimation parameters, which are, peak signal to noise ratio, energy retained, entropy and redundancy. The entropy calculations are done using color histogram and coding programme is developed for estimation of PSNR, ER and redundancy of the compressed image. The results are analyzed and a set of criteria is determined for the acceptability of coding algorithm. Results show that Biorthogonal wavelet filter outperforms the orthogonal one in quality of compressed image but the orthogonal filter is more energy preserving.

A near-lossless approach for medical image compression using visual quantisation and block-based DPCM

For the efficient transmission of medical image datasets over the internet, compression techniques which offer high compression ratio and less computational cost are required. Since the existing lossless techniques have low compression ratios, the alternative approach is to employ near-lossless methods. The existing near-lossless methods like JPEG-LS are context based and computationally intensive. In this paper, a new approach for near-lossless compression of the medical images is proposed. Pre-processing techniques are applied to the input image to generate a visually quantised image. The visually quantised image is encoded using a low complexity block-based lossless differential pulse code modulation coder, followed by the Huffman entropy encoder. The compression performance is compared with the state-of-the-art technique context-based adaptive lossless image coding, by using the objective analysis parameter peak signal to noise ratio and the image fidelity measurement parameter visual signal to noise ratio. Results show the superiority of the proposed technique in terms of the bit rate and visual quality.

Kolmogorov superposition theorem for image compression

The authors present a novel approach for image compression based on an unconventional representation of images. The proposed approach is different from most of the existing techniques in the literature because the compression is not directly performed on the image pixels, but is rather applied to an equivalent monovariate representation of the wavelet-transformed image. More precisely, the authors have considered an adaptation of Kolmogorov superposition theorem proposed by Igelnik and known as the Kolmogorov spline network (KSN), in which the image is approximated by sums and compositions of specific monovariate functions. Using this representation, the authors trade the local connectivity and the traditional line-per-line scanning, in exchange of a more adaptable and univariate representation of images, which allows to tackle the compression tasks in a fundamentally different representation. The contributions lie in the several strategies presented to adapt the KSN algorithm, including the monovariate construction, various simplification strategies, the proposal of a more suitable representation of the original image using wavelets and the integration of this scheme as an additional layer in the JPEG 2000 compression engine, illustrated for numerous images at different bit rates.

Five Modulus Method for Image Compression

Data is compressed by reducing its redundancy, but this also makes the data less reliable, more prone to errors. In this paper a novel approach of image compression based on a new method that has been created for image compression which is called Five Modulus Method (FMM). The new method consists of converting each pixel value in an 8-by-8 block into a multiple of 5 for each of the R, G and B arrays. After that, the new values could be divided by 5 to get new values which are 6-bit length for each pixel and it is less in storage space than the original value which is 8-bits. Also, a new protocol for compression of the new values as a stream of bits has been presented that gives the opportunity to store and transfer the new compressed image easily.

Enhancement of Compression Ratio and Image Quality using ISPIHT with MFHWT

This paper presents a new approach for compression of medical images with MFHWT (Modified Fast Haar Wavelet Transform) and SPIHT (Set Partitioning In Hierarchical Trees). It provide high compression ratio with high picture quality. The Modified Fast Haar Wavelet Transform is used to decompose the image at different frequency levels. It has high multi-resolution characteristics. The CR (compression ratio) of proposed method is better than existing method (SPIHT). Medical images have a number of regions where intensity is changing slowly or even have a constant value. Such regions are compressed with higher compression ratio. In medical applications image distortion is not acceptable. So the quality of the images is also improved in proposed method in terms of PSNR (Peak Signal to Noise Ratio).