Lossless Image Compression Research Articles

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%.

Hybrid Lossless Image Compression Using Wavelet Trans-form and Hierarchical non Linear Prediction

This paper introduces a promising hybrid lossless image compression method by combining the wavelet transform along with a hierarchal non-linear polynomial approximation model to com-press natural and medical images. The test results showed good performance in which the compression ratio is improved about three times or more on average in compered with the results of a non-linear coding system that does not adopt the techniques used in this research.

A LOSSY CODING SCHEME FOR IMAGES BY USING THE HAAR WAVELET TRANSFORM AND THE THEORY OF IDEAL CROSS-POINTS REGIONS

This paper presents Lossy Coding Scheme for Images by Using The Haar Wavelet Transform and The Theory of Cross-points Regions with Ideal Cross-points Regions (HWTICR). The base of this statement is the effect of Gray coding on cross-points which are neighbor to the points of grey levels 2n. After Gray coding these regions always contain only 1-bits or 0-bits depending on the number of each bit plane after bit plane decomposition. The optimization of probability in each bit plane has important effects on encoding and decoding processes of lossless image compression for data transmission. The framework itself is founded upon a wavelet transformed domain, the scheme will show how The Haar Wavelet Transform combines with the theory of Ideal Cross-points Regions to become a lossy coding scheme for images. The goal of the method is to build a lossy coding scheme for images with high compression ratio and low distortion factor in comparison with some other methods. Finally, some initial results of the scheme are also presented and compared to the other methods. The algorithm can be used in medical and photographic imaging.

Image Compression with LZW (Lossless) On Different Image Formats

Image compression is used to reduce bandwidth or storage requirement in image application. Mainly two types of image compression: lossy and lossless image compression. A Lossy Image Compression removes some of the source information content along with the redundancy. While the Lossless Image Compression technique the original source data is reconstructed from the compressed data by restoring the removed redundancy. The reconstructed data is an exact replica of the original source data. Many algorithms are present for lossless image compression like Huffman, rice coding, run length, LZW. LZW is referred to as a substitution or dictionary-based encoding algorithm. The algorithm builds a data dictionary of data occurring in an uncompressed data stream. Patterns of data (substrings) are identified in the data stream and are matched to entries in the dictionary. If the substring is not present in the dictionary, a code phrase is created based on the data content of the substring, and it is stored in the dictionary. The phrase is then written to the compressed output stream. In this paper we see the effect of LZW algorithm on the png, jpg, png, gif, bmp image formats.

Building Materials Defect Monitoring Based on Digital Image Recognition Technology

On the basis of using the vacuum epoxy impregnation technology to obtain microscopic images of building materials, algorithms such as image scaling, index image, image recognition and image identification are respectively designed to identify the pores of the building materials through programs. The images zooming adopts bilinear interpolation to realize the lossless compression of the original microscopic image. The identification of the aperture adopts the method of brightness screening, and the operation of morphological opening and filling. Through the identification of the pores, the material defects can be further analyzed by mesomechanics.

Compression of signs of DCT coefficients for additional lossless compression of JPEG images

One of the main approaches to additional lossless compression of JPEG images is decoding of quantized values of discrete cosine transform (DCT) coefficients and further more effective recompression of the coefficients. Values of amplitudes of DCT coefficients are highly correlated and it is possible to effectively compress them. At the same time, signs of DCT coefficients, which occupy up to 20% of compressed image, are often considered unpredictable. In the paper, a new and effective method for compression of signs of quantized DCT coefficients is proposed. The proposed method takes into account both correlation between DCT coefficients of the same block and correlation between DCT coefficients of neighbor blocks. For each of 64 DCT coefficients, positions of 3 reference coefficients inside the block are determined and stored in the compressed file. Four reference coefficients with fixed positions are used from the neighbor blocks. For all reference coefficients, 15 frequency models to predict signs of a given coefficient are used. All 7 probabilities (that the sign is negative) are mixed by logistic mixing. For test set of JPEG images, we show that the proposed method allows compressing signs of DCT coefficients by 1.1 ... 1.3 times, significantly outperforming nearest analogues.

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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.

HADOOP based image compression and amassed approach for lossless images

Background: This paper develops Hadoop based image compression approach, to solve the problem of low image quality, low compression ratio and high time that occurs during lossless image compression. Method: Lossless image is considered in this paper because in case of lossy compression it leads to information loss which cannot retrieve anymore. By the employment of Hadoop based technique this paper proposes a novel image compression for lossless images. This method makes use of Weiner filter for the cancellation of noise and image blurring. Result: Followed by this, hybrid concepts are employed to perform segmentation and feature extraction. Finally, compression is done with the help of Hadoop map reduce concept. Discussion: Our proposed technique is implemented in MATLAB and therefore the experimental results proved the effectiveness of proposed image compression technique in terms of high compression ratio and low noise ratio when compared with existing techniques.

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

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

A knowledge-embedded lossless image compressing method for high-throughput corrosion experiment

High-throughput experiment refers to carry out a large number of tests and attain various characterizations in one experiment with highly integrated sample or facility, widely adopted in biology, medicine, and materials areas. Consequently, the storing and treating of data bring new challenges because of large amount of real-time data, especially high-resolution images. To improve the storing and treating efficiency of high-throughput image, a knowledge-embedded lossless image compressing method is proposed. Based on the similarity of a series of high-throughput images, it accomplishes the high compression ratio according to the difference between the target images and one reference image. Meanwhile, the knowledge extracted from the image, such as edge information and differences from the reference image, is recorded into the compressed file. The key steps include similarity comparison, edge detection, coordinate transformation, and dictionary encoding. The method has been successfully applied into high-throughput corrosion experiment facility, a typical intelligent cyber-physical system. To evaluate the performance, corrosion metal, face, and flower images are compressed by our method and other lossless image compression methods. The results show that our method has fairly high compression ratio. Moreover, the embedded knowledge can be read directly from the compressed file to support further study.

Low-Complexity and Lossless Image Compression Algorithm for Capsule Endoscopy

Low-Complexity and Lossless Image Compression Algorithm for Capsule Endoscopy

Intellectual Property-Based Lossless Image Compression for Camera Systems [Hardware Matters

This article presents a novel framework for intellectual property (IP)- based lossless image compression used for camera systems. The proposed approach presents two subframeworks: a) two-dimensional (2-D) Haar wavelet transformation (HWT)- based forward pixel calculator IP for image compression and b) 2-D HWTbased inverse pixel calculator IP for image decompression. Each framework is capable of fully compressing and decompressing images through onestage computation during forward and inverse transformations. The proposed approach has been tested on images of critical data sets of medical applications [e.g., computed tomography (CT) images and satellite applications, including NASA images]. Results of compressed and decompressed images along with compression efficiency through the proposed framework for IP-based lossless image compression are also reported in this article.

Multi-resolution Lossless Image Compression for Progressive Transmission and Multiple Decoding Using an Enhanced Edge Adaptive Hierarchical Interpolation

In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.

Approach to implementation of JPEG-LS lossless image compression method

Approach to implementation of JPEG-LS lossless image compression method

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Lossless hyperspectral image compression using bimodal conventional recursive least-squares

ABSTRACTHyperspectral image compression is an important task, where the aim is to store or transmit data in an efficient way. Hyperspectral images are mostly captured by a sensor system that includes multiple imaging sensors covering different regions of the electromagnetic spectrum. Misalignment of multiple imaging sensors produces boresight effect, and this problem can degrade band prediction performance noticeably. Another problem is prediction of blurry band images. In order to gain robustness to these problems, bimodal conventional recursive least-squares (B-CRLS) prediction method is proposed for the lossless compression of hyperspectral images. Two prediction modes are defined: spectral and spatio-spectral. B-CRLS method has a two-step process. First, mode selection is carried out for each band. Afterwards, final band image is predicted by using the selected mode, and the residual images are encoded with an arithmetic encoder. The proposed method is compared to adaptive-length CRLS, fixed-length CRLS, and other well-known prediction methods. Experiments have been performed on uncalibrated and calibrated hyperspectral images. Obtained results show that the proposed method achieves competitive compression performance with the state-of-the-art while providing relatively lower-computation times.

Minimum Description Length Sparse Modeling and Region Merging for Lossless Plenoptic Image Compression

This paper proposes a complete lossless compression method for exploiting the redundancy of rectified light-field data. The light-field data consist of an array of rectified subaperture images, called for short views, which are segmented into regions according to an optimized partition of the central view. Each region of a view is predictively encoded using a specifically designed sparse predictor, exploiting the smoothness of each color component in the current view, and the cross similarities with the other color components and already encoded neighbor views. The views are encoded sequentially, using a spiral scanning order, each view being predicted based on several similar neighbor views. The essential challenge for each predictor becomes choosing the most relevant regressors from a large number of possible regressors belonging to the neighbor views. The proposed solution here is to couple sparse predictor design and minimum description length (MDL) principle, where the data description length is measured by an implementable code length, optimized for a class of probability models. This paper introduces a region merging segmentation under the MDL criterion for partitioning the views into regions having their own specific sparse predictors. In experiments, several fast sparse design methods are considered. The proposed scheme is evaluated over a database of plenoptic images, achieving better lossless compression ratios than straightforward usage of standard image and video compression methods for the spiral sequence of views.

Could post-mortem computed tomography angiography inform cardiopulmonary resuscitation research?

AimFirstly, to develop an optimised chest compression post mortem computed tomography angiography protocol in the adult human during closed chest compression to investigate cardiopulmonary resuscitation blood flow, and secondly to provide preliminary observations of post-mortem anatomical cardiac chamber movement using a novel radiolucent static chest compression device. MethodsVariable volumes of radiological contrast agent were injected intravenously into a series of consented human cadavers. Each cadaver had chest compressions delivered with a LUCAS™2 mechanical chest compressor. Following each cycle of chest compressions, each cadaver was imaged with a Toshiba Aquilion CXL 128 slice computed tomography (CT) scanner to investigate the extent of contrast distribution. A chest compression simulator was then designed and built to allow static CT imaging of 1cm incremental cadaver chest compressions to a depth of 5cm. ResultsMechanical compressions: Ten cases were recruited for the CT angiography component of the study. Two were subsequently excluded from the study at the time of the initial, non-contrast PMCT scan. A further case was recruited in Emergency Department (ED). CT demonstrable antegrade arterial contrast distribution was achieved in 2 cases. The other 7 cases, including that undertaken in ED shortly after death, showed venous retrograde flow. Incremental compressions: Five new cases underwent incremental chest compression imaging. All cases demonstrated compression of the sternum, ribs, atria and great vessels. The right and left ventricles were not compressed, but moved laterally and inferiorly, further into the left chest cavity. The left hemi-diaphragm, stomach and liver moved inferiorly. The sternum, ventricles, hemi-diaphragm, stomach and liver all moved back to their original position on incremental release. ConclusionThe study suggests that with further protocol modification and access to human cadavers as near to death as possible, chest compression post mortem computed angiography (CCPMCTA) could be used as a model for the study of human vascular flow and heart movement during CPR.

A Review of Lossless image Compression for Medical Images with Hoffman Encoding and Differential Pulse Code Modulation (DPCM)

A Review of Lossless image Compression for Medical Images with Hoffman Encoding and Differential Pulse Code Modulation (DPCM)