Image Compression Algorithm Research Articles

Development of a Novel Image Compression Algorithm for Point-of-Care Ultrasound Applications

Development of a Novel Image Compression Algorithm for Point-of-Care Ultrasound Applications

Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need.

Text and Image Compression based on Data Mining Perspective

Data Compression has been one of the enabling technologies for the on-going digital multimedia revolution for decades which resulted in renowned algorithms like Huffman Encoding, LZ77, Gzip, RLE and JPEG etc. Researchers have looked into the character/word based approaches to Text and Image Compression missing out the larger aspect of pattern mining from large databases. The central theme of our compression research focuses on the Compression perspective of Data Mining as suggested by Naren Ramakrishnan et al. wherein efficient versions of seminal algorithms of Text/Image compression are developed using various Frequent Pattern Mining(FPM)/Clustering techniques. This paper proposes a cluster of novel and hybrid efficient text and image compression algorithms employing efficient data structures like Hash and Graphs. We have retrieved optimal set of patterns through pruning which is efficient in terms of database scan/storage space by reducing the code table size. Moreover, a detailed analysis of time and space complexity is performed for some of our approaches and various text structures are proposed. Simulation results over various spare/dense benchmark text corpora indicate 18% to 751% improvement in compression ratio over other state of the art techniques. In Image compression, our results showed up to 45% improvement in compression ratio and up to 40% in image quality efficiency.

Octagon with Diagonal Cross Hair Search Algorithm for Efficient Medical Image Compression

Octagon with Diagonal Cross Hair Search Algorithm for Efficient Medical Image Compression

An Extensive Survey on Compression Algorithm for Neural Network Algorithm

Image compression is critical for productive transmission and cargo space of images. Huge or tremendous request of wireless correspondence and interactive media accessing the multimedia data through Internet is developing violently. The data compression is contance a function. That functions say to Image compression. The objective of image compression is to trim down repetition reiteration of the image information. The image data recalling as a top priority the genuine goal to be able to store or transmit data in a gainful shape. Image compression can be ordered in two sorts lossy or lossless. Lossless compression is from time to time preferred for imitation images for example, specialized illustrations, symbols or funnies. Lossless compression methods may moreover be favored for high regard content, for instance, therapeutic imagery and image filters arranged intended for authentic reasons. Defeating techniques are particularly appropriate for regular ordinary images, for example, photographs in applications where slight defeat of devotion is worthy to achieve a liberal lessening in bit rate. This work wished-for a near report and overview on image compression algorithms.

Effective lossy and lossless color image compression with Multilayer Perceptron

This paper presents the effective lossy and lossless color image compression algorithm with Multilayer perceptron. The parallel structure of neural network and the concept of image compression combined to yield a better reconstructed image with constant bit rate and less computation complexity. Original color image component has been divided into 8x8 blocks. The discrete cosine transform (DCT) applied on each block for lossy compression or discrete wavelet transform (DWT) applied for lossless image compression. The output coefficient values have been normalized by using mod function. These normalized vectors have been passed to Multilayer Perceptron (MLP). This proposed method implements the Back propagation neural network (BPNN) which is suitable for compression process with less convergence time. Performance of the proposed compression work is evaluated based on three ways. First one compared the performance of lossy and lossless compression with BPNN. Second one, evaluated based on different sized hidden layers and proved that increased neurons in hidden layer has been preserved the brightness of an image. Third, the evaluation based on three different types of activation function and the result shows that each function has its own merit. Proposed algorithm has been competed with existing JPEG color compression algorithm based on PSNR measurement. Resultant value denotes that the proposed method well performed to produce the better reconstructed image with PSNR value approximately increased by 21.62%.

Encryption of Stereo Images after Compression by Advanced Encryption Standard (AES)

New partial encryption schemes are proposed, in which a secure encryption algorithm is used to encrypt only part of the compressed data. Partial encryption applied after application of image compression algorithm. Only 0.0244%-25% of the original data isencrypted for two pairs of dif-ferent grayscale imageswiththe size (256 ´ 256) pixels. As a result, we see a significant reduction of time in the stage of encryption and decryption. In the compression step, the Orthogonal Search Algorithm (OSA) for motion estimation (the dif-ferent between stereo images) is used. The resulting disparity vector and the remaining image were compressed by Discrete Cosine Transform (DCT), Quantization and arithmetic encoding. The image compressed was encrypted by Advanced Encryption Standard (AES). The images were then decoded and were compared with the original images. Experimental results showed good results in terms of Peak Signal-to-Noise Ratio (PSNR), Com-pression Ratio (CR) and processing time. The proposed partial encryption schemes are fast, se-cure and do not reduce the compression performance of the underlying selected compression methods

Analisis Pengaruh Kompresi Citra Fundus terhadap Kinerja Sistem Automated Microanerysm Detections

Diabetes is one of the most serious diseases that commonly suffered by people around the world, including Indonesia. Early symptoms of diabetes could be observed from various indicators, one of which is through the retina. Retina conditions is affected by diabetics and when remain unproperly threated could lead to blindness. This retinal disorders due to diabetes is normally called Diabetic Retinopathy (DR). One method that able to distinguish and detect DR is microaneurysm detection. This method requires good quality of retinal images. However, in certain areas such as rural areas, this requirement may difficult to meet due to lack of adequate infrastructure. One solution that may overcome this problem is to compress the images. In this paper, image compression algorithms were applied to the retinal image, and then used to detect microaneuryms through Deep Learning-based systems. The result shows that the most stable and appropriate algorithm is PNG, which is able to correctly classify around 83% in accuracy with 5,5% variance.

Hybrid two-dimensional dual tree—biorthogonal wavelet transform and discrete wavelet transform with fuzzy inference filter for robust remote sensing image compression

Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. In particular, remote sensing makes it possible to collect image data on dangerous or inaccessible areas (in Roy et al. Signal Process 128: 262–273, 2016). The methods are introduced in previous research for the efficient image compression with less error rate. The existing method is named as 2D-dual tree-complex wavelet transform (2D-DT-CWT) with fuzzy inference filter (FIF) based image compression algorithm which is used for the aid of remote sensing image compression. However it has issue with time complexity and lack in robust compression ratios. To avoid the above mentioned issues, in the proposed system, the approach enhanced called as hybrid 2D-oriented biorthogonal wavelet transform (2D-BWT) by using Windowed all phase digital filter (WAPDF) based on discrete wavelet transform (DWT) for robust image compression algorithm. The proposed system contains modules such as image compression using 2D-DWT, 2D-BWT using WAPDF for improving transformation, coefficient selection using FIF. Then context-adaptive binary arithmetic coding (CABAC) with lattice vector quantization (LVQ) is proposed for encoding the wavelet significant coefficients. DWT is used to focus on the provision of high quality compression images and BWT is used to improve the transformation process. The experimental results show that hybrid-2D-BDWT can help in significant improvement of the transform coding gain, specifically for remote sensing images having good resolution. In this research, the comparison of the proposed work is done with the existing 2D-oriented wavelet transform (2D-OWT) and 2D-DT-CWT. Also, the new compression method is simple, and the memory requirement in the operation process is very less. It provides robust image compression ratio and high quality images using transformation methods.

A Parallel Algorithm for Wavelet Transform-Based Color Image Compression

Abstract Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution-based strategy is widely used in calculating the wavelet transform of the image. A convolution-based wavelet transform consists of a large number of multiplications and additions. A color image consists of a two-dimensional matrix each for red, green, and blue colors. An ordinary way to calculate the wavelet transform of a color image includes calculating the transform of the intensity matrix of the red, green, and blue components. In this article, we present a parallel algorithm for calculating the convolution-based wavelet transform of the red, green, and blue intensity components simultaneously in color images, which can run on commonly used processors. This means that it needs no extra hardware. The results are also compared to the nonparallel algorithm based on compression time, mean square error, compression ratio, and peak signal-to-noise ratio. Complexity analysis and comparative complexity analysis with some other papers are also shown here.

Images compression by using cubic spline-functions methods

The object of research is image compression algorithms based on mathematical methods. The main problem with image compression is loss of quality during recovery. The approach is proposed in which the user can determine the quality of the reconstructed image itself. This is achieved due to the use of the spline interpolation method, which allows to set the compression ratio, thus controlling the quality of the decoded image.The use of the spline function for image compression makes it possible to reduce the processing time of files due to the simplicity of the mathematical model of the algorithm. Given the accuracy of the restored image, the algorithm determines the size of the compressed file, depending on the color scale.As a result of the analysis of the proposed development, the compression coefficients are shown, which show that the size of the compressed image can be smaller than the original image by 50–70 %. The decoding is performed using known spline function coefficients. The result is compared with the original file. The difference between the intensity of the points of the source and decoded images determines the quality of the restoration.An algorithm is obtained that allows one to specify the accuracy of the reconstructed image. This result depends on the weighting coefficients of the spline function, which affect the accuracy of the construction of the approximating polynomial. A feature of the proposed approach is the ability of the user to specify the accuracy and quality of the image after decoding. This is achieved due to the fact that points close in intensity value are restored with a small error.In this paper, let's propose an approach involving the sequential extraction of blocks of points of equal intensity. For the selected blocks, an approximating polynomial is constructed based on the spline function, and the coefficients of the polynomial are transferred to a file containing information for image reconstruction. So it is possible to obtain large compression ratios by building a polynomial for blocks containing points that are close in intensity.

Validation of Image Compression Algorithms using Neural Network

Validation of Image Compression Algorithms using Neural Network

Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

Lossless Compression of Color Filter Array Mosaic Images With Visualization via JPEG 2000

Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images, which must be (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible, it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Thus, data compression is of interest. Several compression methods for mosaic images have been proposed in the literature. However, they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper, a novel compression pipeline that removes these requirements is proposed. Specifically, mosaic images can be losslessly recovered from the resulting compressed files, and, more significantly, images can be directly viewed (decompressed and developed) using only a JPEG 2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality, while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.

Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

Image set compression has recently become an active research topic due to the explosion of digital photographs. In order to efficiently compress the image sets of similar images with moving objects, in this paper, we propose a novel algorithm for image set compression using multiple reference images. First, for an image set, its depth-constrained minimum arborescence is generated. We then present a reference image candidate determination method to build the reference image candidates for the images of the set. Furthermore, we propose a rate-distortion optimized multiple reference image selection method. This method compares the correlation between every image and each of its reference image candidates to produce its multiple reference images. Finally, compressed image data are achieved by employing block-based motion compensation and residue coding. In addition, we also give a new way of access to images to keep the same access delay with single reference image-based schemes. Compared with the state-of-the-art image compression algorithms, experimental results show that our proposed algorithm can significantly improve the image compression performance.

An alphabet reduction algorithm for lossless compression of images with sparse histograms

In this paper, we propose a new adaptive arithmetic coding for lossless image compression applying an alphabet reduction algorithm. The algorithm is a reduction mechanism of the alphabet set within each block by assigning to each one an as small as possible symbol set including all the really present symbols called active symbols, instead of using the nominal alphabet set. The method can be considered as away to address the well-known zero-frequency problem which appears especially for images with sparse and locally sparse histograms. The analytical expression of the expected gain in terms of compression efficiency when using the block active symbol sets is derived. We show experimentally that the proposed method, in conjunction with adaptive arithmetic coding order-0 model applied for images with sparse and locally sparse histograms, provides promising compression ratios and outperforms several state-of-the-art lossless image compression standards such as JPEG2000, JPEG-LS and CALIC.

An alphabet reduction algorithm for lossless compression of images with sparse histograms

An alphabet reduction algorithm for lossless compression of images with sparse histograms

The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

Gray scale and colour medical image compressed using hybrid contourlet biorthogonal CDF lifting scheme, bandelet and quincunx wavelet transforms: a comparative study

The Quincunx wavelet, the lifting scheme wavelet and the second generation bandelet transform are a new method to offer an optimal representation for image geometric. In the field of medical diagnostics, interested parties have resorted increasingly to medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality, but the quality of the image is itself dependent on a number of factors including primarily the processing that an image must undergo to enhance its quality. The quality evaluation of compressed image is necessary to judge the performance of a compression method. This paper introduces an algorithm for medical image compression based on hybrid non-subsampled contourlet (NSCT) and quincunx wavelet transforms (QWT) coupled with SPIHT coding algorithm, of which we present the objective measurements (PSNR, EDGE, WPSNR, MSSIM, VIF, and WSNR) in order to evaluate the quality of the image.

Efficient image compression and decompression algorithms for OCR systems

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the ?Nikola Tesla Museum? in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system.