Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.

https://www.mdpi.com/2073-8994/11/10/1274/pdf

Lossless Image Compression Techniques: A State-of-the-Art Survey

thresholding is a well known image segmentation procedure extensively attempted to obtain binary image from the gray level image. In this article, histogram based bi-level and multi-level segmentation is proposed for gray scale images using Bat Algorithm (BA). The optimal thresholds are attained by maximizing Otsu's between class variance function. The performance of BA is demonstrated by considering five benchmark (512 x 512) images and compared it with the existing algorithms such as Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) existing in the literature. The performance assessment between algorithms is carried out using prevailing parameters such as objective function, Peak Signal to Noise Ratio (PSNR), and Structural Dissimilarity (SSIM) index. The results evident that BA provides better objective function, PSNR and SSIM compared to PSO, and BFO considered in this study.

/pdf/gray-level-histogram-based-multilevel-threshold-selection-30q2gd9iaa.pdf

Gray-Level Histogram based Multilevel Threshold Selection with Bat Algorithm

An image compression method eradicates redundant and/or unrelated information, and resourcefully encodes leftovers. Practically, it is frequently essential to toss away both non redundant information and relevant information to attain the essential compression. In any case, the ploy is discovering methods that permit important information to be resourcefully extracted and represented. This paper copes with dissimilar compression methods for comprising the information in an image. The information can be compressed by means of Lossy techniques such as Quantization, Transform coding, Block Transform Coding or Lossless techniques such as Run Length Coding, Lossless Predictive Coding, Multi-resolution Coding. All these techniques have been discussed in this paper and the performance of any technique/method is analyzed on various parameters like MSE and PSNR.

/pdf/a-review-of-image-compression-techniques-3snbdba3ey.pdf

A Review of Image Compression Techniques

Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.

/pdf/lossy-image-compression-based-on-prediction-error-and-vector-43fxsszeuv.pdf

Lossy image compression based on prediction error and vector quantisation

Modeling latent variables with priors and hyperpriors is an essential problem in variational image compression. Formally, trade-off between rate and distortion is handled well if priors and hyperpriors precisely describe latent variables. Current practices only adopt univariate priors and process each variable individually. However, we find inter-correlations and intra-correlations exist when observing latent variables in a vectorized perspective. These findings reveal visual redundancies to improve rate-distortion performance and parallel processing ability to speed up compression. This encourages us to propose a novel vectorized prior. Specifically, a multivariate Gaussian mixture is proposed with means and covariances to be estimated. Then, a novel probabilistic vector quantization is utilized to effectively approximate means, and remaining covariances are further induced to a unified mixture and solved by cascaded estimation without context models involved. Furthermore, code books involved in quantization are extended to multi-codebooks for complexity reduction, which formulates an efficient compression procedure. Extensive experiments on benchmark datasets against state-of-the-art indicate our model has better rate-distortion performance and an impressive 3.18x compression speed up, giving us the ability to perform real-time, high-quality variational image compression in practice. Our source code is publicly available at https://github.com/xiaosu-zhu/McQuic.

Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression

This paper addresses about various image compression techniques. On the basis of analyzing the various image compression techniques this paper presents a survey of existing research papers. In this paper we analyze different types of existing method of image compression. Compression of an image is significantly different then compression of binary raw data. To solve these use different types of techniques for image compression. Now there is question may be arise that how to image compress and which types of technique is used. For this purpose there are basically two types are method are introduced namely lossless and lossy image compression techniques. In present time some other techniques are added with basic method. In some area neural network genetic algorithms are used for image compression. 
Keywords-Image Compression; Lossless; Lossy; Redundancy; Benefits of Compression.

A Survey: Various Techniques of Image Compression.

The utility of medical image increases due to serious disease prediction such as brain stroke and lung cancer. The size of medical image required large amount of memory and bandwidth for storage and transmission. For the lossless compression of medical image some standard algorithm are used such as JPEG, composite and Wavelet based. These entire algorithms perform good compression ratio and PSNR value. But the redundant structure of medical image reduces the compression ratio and decreases the value of PSNR and suffered quality of medical image compression. In this paper we proposed a structure reference selection process for collecting redundant frame of structure for compression. The collection of redundant frame structure is performed by particle swarm optimization algorithm. Particle swarm optimization is searching technique. For the generation of structure of frame we used integer wavelet transform function. The proposed algorithm is implemented through MATLAB software and is compared with composite algorithm, SOM algorithm and JPEG compression technique. General Terms Integer wavelet transforms, Reference Frame, Image Compression et. al.

/pdf/a-novel-medical-image-compression-technique-based-on-2d10igs5xp.pdf

Gaurav Vijayvargiya

Papers

A Survey: Various Techniques of Image Compression.

A Novel Medical Image Compression Technique based on Structure Reference Selection using Integer Wavelet Transform Function and PSO Algorithm