Binary Image Compression Research Articles

Lossless Compression of Bilevel ROI Maps of Hyperspectral Images Using Optimization Algorithms

In this letter, we propose a novel scheme for the compression of the bilevel region of interest (ROI) maps of hyperspectral images. The scheme can achieve high compression ratios by novel ways of partitioning the intricate regions of the image into smaller blocks. We first analyzed the effect of a low-order linear predictive model on the original image. The analysis showed that linear prediction results in lower-entropy residual images compared to the original images, which in turn helps in improving the compression efficiency. We then use the optimization algorithm that finds the best combination of scan directions for the nonzero blocks. It is shown to offer increasingly better compression with additional iterations. The simulation results on various data sets show that our scheme outperforms the international standard for binary image compression [joint bi-level image expert (JBIG2)].

Comparing Freeman Chain Code 4 Adjacency Algorithm and LZMA Algorithm in Binary Image Compression

Image is one product that has experienced a significant increase in quality. Insufficient internet capacity makes it difficult to access large images. Image compression is one way to solve this problem, but image compression can make image quality degrade. This research uses lossless compression algorithms, namely Chain Code Algorithm and LZMA Algorithm, to reduce the quality of the image and can be adjusted the tolerance value manually. In the application that will be created, the user can select the image to be compressed, then compress it according to the tolerance value that has been set and with the compression result of a text file, where the text file can be extracted and reconstructed into an image. There will be a preview of the results of the image compression and a description of the size and quality reduction levels. The conclusion of this research is that Chain Code Algorithm and LZMA Algorithm are able to compress image file sizes / images with extensions .jpg, .png. .bmp with a success rate of 90% from 50 tests.

Modified OFS-RDS bat algorithm for IFS encoding of bitmap fractal binary images

This work is an extension of a previous paper (presented at the Cyberworlds 2019 conference) introducing a new method for fractal compression of bitmap binary images. That work is now extended and enhanced through three new valuable features: (1) the bat algorithm is replaced by an improved version based on optimal forage strategy (OFS) and random disturbance strategy (RDS); (2) the inclusion of new similarity metrics; and (3) the consideration of a variable number of contractive maps, whose value can change dynamically over the population and over the iterations. The first feature improves the search capability of the method, the second one improves the reconstruction accuracy, and the third one computes the optimal number of contractive maps automatically. This new scheme is applied to a benchmark of two binary fractal images exhibiting a complex and irregular fractal shape. The graphical and numerical results show that the method performs very well, being able to reconstruct the input images with high accuracy. It also computes the optimal number of contractive maps in a fully automatic way. A comparative work with other alternative methods described in the literature is also carried out. It shows that the presented method outperforms the previous approaches significantly.

A Fast and Efficient Lossless Compression Technique for Greyscale Images

The growth of cloud based remote healthcare and diagnosis services has resulted, Medical Service Providers (MSP) to share diagnositic data across diverse environement. This medical data are accessed across diverse platforms, such as, mobile and web services which needs huge memory for storage. Compression technique helps to address and solve storage requirements and provides for sharing medical data over transmission medium. Loss of data is not acceptable for medical image processing. As a result, this work considers lossless compression for medical in particular and in general any greyscale images. Modified Huffman encoding (MH) is one of the widely used technique for achieving lossless compression. However, due to longer bit length of codewords the existing Modified Huffman (MH) encoding technique is not efficient for medical imaging processing. Firstly, this work presents Modified Refined Huffman (MRH) for performing compression of greyscale and binary images by using diagonal scanning method. Secondly, to minimize the computing time parallel encoding method is used. Experiments are conducted for wide variety of images and performance is evaluated in terms of Compression Ratio, Computation Time and Memory Utilization. The proposed MRH achieves significant performance improvement in terms of Compression Ratio, Computation Time and Memory Usage over its state-of-the-art techniques, such as, LZW, CCITT G4, JBIG2 and Levenberg–Marquardt (LM) Neural Network algorithm. The overall results achieved show the applicability of MRH for different application services.

Lossless Text Image Compression using Two Dimensional Run Length Encoding

Text images are used in many types of conventional data communication where texts are not directly represented by digital character such as ASCII but represented by an image, for instance facsimile file or scanned documents. We propose a combination of Run Length Encoding (RLE) and Huffman coding for two dimensional binary image compression namely 2DRLE. Firstly, each row in an image is read sequentially. Each consecutive recurring row is kept once and the number of occurrences is stored. Secondly, the same procedure is performed column-wise to the image produced by the first stage to obtain an image without consecutive recurring row and column. The image from the last stage is then compressed using Huffman coding. The experiment shows that the 2DRLE achieves a higher compression ratio than conventional Huffman coding for image by achieving more than 8:1 of compression ratio without any distortion.

A New MRF-Based Lossy Compression for Encrypted Binary Images

Although there exist many researches on the compression of original non-encrypted binary images, few approaches focus on the compression of encrypted binary images. As binary images like contract, signature, halftone images are still used widely in practice, how to compress efficiently encrypted binary images in a lossy way deserves further exploration. To this end, this paper develops a lossy compression scheme for encrypted binary images by exploiting the Markov random field (MRF) model. Considering that the third-party in scenarios of cloud or distributed computing cannot access to the encryption key, we develop the concatenated down-sampling and LDPC-based encoding to perform the compression, in which four different down-sampling methods are designed to facilitate improving the quality of reconstructed image. In reconstruction, we first formulate the lossy reconstruction from the encrypted and compressed binary image as an optimization problem, and then build a joint factor graph involving the LDPC-decoding, decryption, and MRF to solve this optimization problem, in which the MRF is exploited to well infer pixels discarded in the down-sampling process. By adapting the sum-product algorithm (SPA) to the constructed joint factor graph for lossy reconstruction (JFG-LR) and running the adapted SPA on the JFG-LR, we thus recover the original binary image in a lossy way. By integrating the stream-cipher-based encryption, the down-sampling and LDPC-based compression, and the JFG-LR-involved reconstruction, we thus propose a new lossy compression scheme for encrypted binary images. Experimental results show that the proposed scheme achieves desirable compression efficiency, which is comparable to or even better than that of the JBIG2 with the original unencrypted binary image as input.

Binary medical image compression using the volumetric run-length approach

ABSTRACTImage compression has become an inevitable tool along with the advancing medical data acquisition and telemedicine systems. The run-length encoding (RLE), one of the most effective and practical lossless compression techniques, is widely used in two-dimensional space with common scanning forms such as zigzag and linear. In this study, an algorithm which takes advantage of the potential simplicity of the run-length algorithm is devised in a volumetric approach for three-dimensional (3D) binary medical data. The proposed algorithm, namely 3D-RLE, being different from the two-dimensional approach that utilizes only intra-slice correlations, is designed to compress binary volumetric data by employing also the inter-slice correlation between the voxels. Furthermore, it is extended to several scanning forms such as Hilbert and perimeter to determine an optimal scanning procedure coherent with the morphology of the segmented organ in data. The algorithm is employed on four datasets for a comprehensive assessment. Numerical simulation results demonstrated that the performance of the algorithm is 1:30 better than those of the state-of-the-art techniques, on average.

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

Morphological Decomposition and Compression of Binary Images via a Minimum Set Cover Algorithm

In this paper, by a novel morphological decomposition method, we propose an efficient compression approach. Our presented decomposition arises from solving a minimum set cover problem (MSCP) obtained from the image skeleton data. We first use the skeleton pixels to create a collection of blocks which cover the foreground. The given blocks are both overlapped and too many. Hence, in order to find the minimum number of the blocks that cover the foreground, we form an MCSP. To solve this problem, we present a new algorithm of which accuracy is better than those of the known methods in the literatures. Also, its error analysis is studied to obtain an error bound. In the sequel, we present a fast algorithm which include an extra parameter by which the relation between the accuracy and CPU time can be controlled. Finally, several examples are given to confirm the efficiency of our approach.

Eliminating Switching Components in Binary Matrices by 0-1 Flips and Column Permutations

Analysis of patterns in binary matrices plays a vital role in numerous applications of computer science. One of the most essential patterns of such matrices are the so called switching components, where the number and location of the components gives valuable information about the binary matrix. One way to measure the effect of switching components in a binary matrix is counting the number of 0-s which have to be replaced with 1-s in order to eliminate the switching components. However, finding the minimal number of 0-1 flips is generally an NP-complete problem. We present two novel-type heuristics for the above problem and show via experiments that they outperform the formerly proposed ones, both in optimality and in running time. We also show how to use those heuristics for determining the so-called nestedness level of a matrix, and how to use the flips for binary image compression.

A Hybrid Lossless-Lossy Binary Image Compression Scheme

In this paper, the authors present a binary image compression scheme that can be used either for lossless or lossy compression requirements. This scheme contains five new contributions. The lossless component of the scheme partitions the input image into a number of non-overlapping rectangles using a new line-by-line method. The upper-left and the lower-right vertices of each rectangle are identified and the coordinates of which are efficiently encoded using three methods of representation and compression. The lossy component, on the other hand, provides higher compression through two techniques. 1) It reduces the number of rectangles from the input image using our mathematical regression models. These mathematical models guarantees image quality so that rectangular reduction should not produce visual distortion in the image. The mathematical models have been obtained through subjective tests and regression analysis on a large set of binary images. 2) Further compression gain is achieved through discarding isolated pixels and 1-pixel rectangles from the image. Simulation results show that the proposed schemes provide significant improvements over previously published work for both the lossy and the lossless components.

Compression of binary graphics using context simulation

The compression of binary bitmap images based on statistical coding with context simulation is considered. Development trends for models and methods of context simulation for an efficient algorithm of compressing binary graphics are discussed. An efficient algorithm of compressing binary graphics is presented.

Multimedia Web Services Performance: Analysis and Quantification of Binary Data Compression

Multimedia Web services constitute a considerable load on Internet bandwidth and on Web servers. With the increasing demand of multimedia Web services, the need to maintain a respectable QoS is a major concern for multimedia Web services’ providers. Binary data compression, lossy and lossless, increases performance gain and maintains QoS, at large and small-scale implementations. At the expense of increased CPU processing time, data compression provides a reliable solution to sustain QoS parameters such as response time, download time and server delay where bandwidth is limited. This paper presents a detailed analysis, which is based upon realistic and representative data, in order to quantify the performance gain for multimedia Web services due to the application of binary image compression. To provide data for our analysis method, we have built an image-retrieval Web service that provides a set of images in both compressed and non-compressed form. The work in this paper promotes the utilisation of multimedia compression techniques and proposes the integration of multimedia compression techniques in current Web standards such as HTTP. The analysis method and results can be extended and adapted to various service and network models, such as wireless LAN, Ad-Hoc, mobile networks, IP-telephony and distributed systems.

Resolution Scalable Image Coding With Reversible Cellular Automata

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions.

Compression of Whole Genome Alignments

Recent advances in DNA sequencing technology have caused an exponential growth of publicly available genomic sequence data. A particularly voluminous, frequently used static data set are whole genome alignments. The first lossless compression algorithm for such data sets based on well-established statistical evolutionary models and prediction techniques from lossless binary image compression is introduced. The compression rate is improved by a factor of 1.6 compared to the currently used Lempel-Ziv (LZ) compression.

An improved pattern matching technique for lossy/lossless compression of binary printed Farsi and Arabic textual images

PurposeThe purpose of this paper is to propose a lossy/lossless binary textual image compression method based on an improved pattern matching (PM) technique.Design/methodology/approachIn the Farsi/Arabic script, contrary to the printed Latin script, letters usually attach together and produce various patterns. Hence, some patterns are fully or partially subsets of some others. Two new ideas are proposed here. First, the number of library prototypes is reduced by detecting and then removing the fully or partially similar prototypes. Second, a new effective pattern encoding scheme is proposed for all types of patterns including text and graphics. The new encoding scheme has two operation modes of chain coding and soft PM, depending on the ratio of the pattern area to its chain code effective length. In order to encode the number sequences, the authors have modified the multi‐symbol QM‐coder. The proposed method has three levels for the lossy compression. Each level, in its turn, further increases the compression ratio. The first level includes applying some processing in the chain code domain such as omission of small patterns and holes, omission of inner holes of characters, and smoothing the boundaries of the patterns. The second level includes the selective pixel reversal technique, and the third level includes using the proposed method of prioritizing the residual patterns for encoding, with respect to their degree of compactness.FindingsExperimental results show that the compression performance of the proposed method is considerably better than that of the best existing binary textual image compression methods as high as 1.6‐3 times in the lossy case and 1.3‐2.4 times in the lossless case at 300 dpi. The maximum compression ratios are achieved for Farsi and Arabic textual images.Research limitations/implicationsOnly the binary printed typeset textual images are considered.Practical implicationsThe proposed method has a high‐compression ratio for archiving and storage applications.Originality/valueTo the authors' best knowledge, the existing textual image compression methods or standards have not so far exploited the property of full or partial similarity of prototypes for increasing the compression ratio for any scripts. Also, the idea of combining the boundary description methods with the run‐length and arithmetic coding techniques has not so far been used.

A NAM Representation Method for Data Compression of Binary Images

A representation method using the non-symmetry and anti-packing model (NAM) for data compression of binary images is presented. The NAM representation algorithm is compared with the popular linear quadtree and run length encoding algorithms. Theoretical and experimental results show that the algorithm has a higher compression ratio for both lossy and lossless cases of binary images and better reconstructed quality for the lossy case.

An improved fractal image compression approach by using iterated function system and genetic algorithm

This paper presents an improved method of generating a binary image affine IFS (iterated function system) by using genetic algorithm. We adopt a natural variable-length genotype encoding to represent an individual. The multiobject fitness function is also applied in this algorithm. In addition, a distributed version of the binary image compression algorithm is implemented. Both theoretical analysis and experimental results show a higher compression ratio with better quality images by using the proposed algorithm.

Nonlinear Discrete Wavelet Transforms over Finite Sets and an Application to Binary Image Compression

The discrete wavelet transform was originally a linear operator that works on signals that are modeled as functions from the integers into the real or complex numbers. However, many signals have discrete function values. This paper builds on two recent developments: the extension of the discrete wavelet transform to finite-valued signals and the research of nonlinear wavelet transforms triggered by the introduction of the lifting scheme by Sweldens. It defines discrete wavelet transforms as bijective, translation-invariant decompositions of signals that are functions from the integers into any finite set. Such transforms are essentially nonlinear, but they can be calculated very time efficiently since only discrete arithmetic is needed. Properties of these generalized discrete wavelet transforms are given along with an elaborate example of such a transform. In addition, the paper presents some ideas to find explicit examples of discrete wavelet transforms over finite sets. These ideas are used to show that, in case the finite set is a ring, there are much more nonlinear transforms than linear transforms. Finally, the paper exploits this increased number of transforms to do lossless compression of binary images.

Quadtree and statistical model-based lossless binary image compression method

This paper presents a new lossless binary image compression method. The method consists of four stages: preprocessing, quadtree compressing, run length compressing and statistical model-based compressing. The preprocessing stage is to reduce the entropy of a binary image. In the quadtree compressing stage, a breadth first traversal (BFT) linear quadtree is used to compress the image. The run length compressing stage uses the run length method to encode the tree list and colour list of the BFT linear quadtree. The statistical model-based compressing stage applies the Huffman coding algorithm to compress the remaining data of the BFT linear quadtree further. Experimental results show that this method can provide an impressive compression result.