Hybrid Algorithm for Lossless Image Compression using Simple Selective Scan order with Bit Plane Slicing

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.

In this article we report on a study of how to use the context tree weighting (CTW) algorithm for lossless image compression. This algorithm has been shown to perform optimally, in terms of redundancy, for a wide class of data sources. Our study shows that this algorithm can successfully be applied to image compression even in its basic form. We also report on possible modifications of the basic CTW algorithm to let it work more efficiently for image data. Our research is briefly focussed on the compression of medical gray scale images.

With the success of deep learning in computer vision applications, deep learning based algorithms have also been proposed for image processing applications. One such application is lossless image compression. Most traditional lossless image compression algorithms use pixel-by-pixel processing algorithms. In the pixel-by-pixel algorithms, each pixel is predicted from the previously coded neighbor pixels and the prediction error is compressed without loss. Deep learning based lossless image compression algorithms can be categorized into two categories, namely prior based algorithms and pixel-by-pixel (or masked convolution based) algorithms. In the pixel-by-pixel algorithms, each pixel's probability distribution is obtained by processing the previously coded neighboring pixels with a neural network, which is then used by an arithmetic coder for lossless compression. This paper explores a deep learning based architecture, which utilizes masked convolutions, to model probability distributions of pixels and also presents a method to improve the parallelization of the algorithms. The obtained compression performance is competitive and is compared to both state-of-the art traditional and deep learning based methods.

Lossless gray scale image compression is necessary for many purposes, such as medical imaging, image databases and so on. Lossy images are important as well, because of the high compression ratio. The authors propose a lossless image compression scheme using a lossy image generated with the JPEG-DCT scheme. The concept is, send a JPEG-compressed lossy image primarily, then send residual information and reconstruct the original image using both the lossy image and residual information. 3D adaptive prediction and adaptive arithmetic coding are used, which fully use the statistical parameters of the distribution of the symbol source. The optimal number of neighbor pixels and lossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the original lossless algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Although subband and wavelet coding have been proved to be very efficient for lossy image compression, it is generally not possible to represent the output of most subband/wavelet filters by a limited number of bits and yet guarantee perfect reconstruction. This is a fundamental problem when applying subband and wavelet coding to lossless image compression. This paper presents a solution to this problem by an investigation of interpolative subband coding. Based on an evaluation of the decorrelation performance of some typical image interpolation techniques, a new interpolative coding algorithm has been proposed. The new algorithm has been shown to be superior to two related algorithms, the hierarchical interpolation algorithm and the recently published minimum entropy pyramid algorithm.

Lossless image compression plays a vital role in improving data storage and transmission efficiency without compromising data integrity. However, the throughput of current lossless compression and decompression systems remains limited and is unable to meet the growing demands of high-speed data transfer. To address this challenge, a previously proposed hybrid lossless compression and decompression algorithm has been implemented on an FPGA platform. This implementation significantly improves processing speed and efficiency. A multi-core system architecture is introduced, utilizing the processing system (PS) and programmable logic (PL) of a Xilinx Zynq-706 evaluation board. The PS handles coordination. The PL performs compression and decompression using multiple cores. Each core can process up to eight image tiles at the same time. The compression process is designed with a four-stage pipeline, and decompression is managed by a dynamic state machine to ensure optimized control. The parallel architecture and innovative algorithm design enable high-throughput operation, achieving compression and decompression rates of 480 Msubpixels/s and 372 Msubpixels/s, respectively. Through this work, a practical and high-performance solution for real-time lossless image compression is demonstrated.

We present a novel lossless image compression algorithm. It achieves better compression than popular lossless image formats like PNG and lossless JPEG 2000. Existing image formats have specific strengths and weaknesses: e.g. JPEG works well for photographs, PNG works well for line drawings or images with few distinct colors. For any type of image, our method performs as good or better (on average) than any of the existing image formats for lossless compression. Interlacing is improved compared to PNG, making the format suitable for progressive decoding and responsive web design.

In technological developments, it also affects the size of the data from a file, especially in transmitting JPEG images or images. Digital images have a size or capacity that is large enough so that the image transmission process often experiences problems due to its large enough capacity. Large data sizes also require large storage space, therefore additional solutions are needed to save space on storage media, such as reducing the size of the data to be stored. Knowing the value of the speed and efficiency of the JPEG image compression process needs to remember so many compression methods that exist today, one of which is the FELICS method. Image compression is carried out on images aimed at reducing the redundancy of existing data in the image so that it can be stored or transmitted efficiently. The FELICS method, which stands for Fast Compression & Lossless Image System, is a lossless image compression algorithm that performs 5 times faster than the original lossless JPEG codec and produces a compression ratio of the same value. So through this research the writer wants to carry out the analysis process of the FELICS method. The Fast Compression & Lossless Image System method works very well on images and produces high ratio values. Keywords : Analysis, Image, Compression, FELICS Method

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

As medical imaging facilities move towards film-less imaging technology, robust image compression systems are starting to play a key role. Conventional storage and transmission of large-scale raw medical image datasets can be very expensive and time-consuming. Recently, we proposed a memory-assisted lossless image compression algorithm based on Principal Component Analysis(PCA). In this paper, we further improve the performance of the algorithm in two different directions: Firstly, we replace PC A with NMF (Non Negative Matrix Factorization). NMF has several advantages in representing images with an image-like basis, results in sparse factors, and provides better user control over iterations. Secondly, we expand the single-level model with a new multilevel decomposition/projection framework to further reduce entropy of residual images. Our experimental results on X-ray images confirm that both modifications provide significant improvements over the single level PCA based algorithm as well as existing non-memory based techniques.

In this paper we propose a DWT-based perceptually lossless image compression algorithm and architecture. Many image-capturing devices acquire images in subsampled R-G-B format and have real-time constraints which prohibit conversion to other formats, generally preferred for image compression, such as Y-Cb-Cr. Therefore, we propose a compression algorithm which works performed directly on the subsampled R-G-B data. The scheme exploits the correlation between the color planes and the perceptual sensitivity of the human visual system in order to improve the compression ratio. Moreover it can be easily mapped into an integrated fast architecture yielding very high throughput.

Problem statement: Existing lossless image compression schemes attempt to do prediction in an image data using their Local Binary Pattern ( LBP) and their spatial neighborhood based techniques. In the previous techniques such as Vec tor Quantization (VQ) and Gradient Adjusted Prediction (GAP) the texture and non-texture region s are not classified separately. Texture and Non- texture images prophecy has been a key factor in ef ficient lossless image compression. Hence, there is a need to develop a more efficient image prediction scheme to exploit these texture components. Approach: In this research, an efficient visual quality tech nique for image compression is proposed. The image is classified into texture and non-textur e regions by using an Artificial Neural Network (ANN) Classifier. The texture region is encoded wit h the Similar Block Matching (SBM) encoder and the non-texture region is encoded with SPIHT encoding. Results: The proposed texture prediction based compression is compared with the existing compression techniques such as H.264 and JPEG. From the result it reveals that the Peak Signal to Noise Ratio (PSNR) values of all the test images is higher in the proposed technique as compared to JPE G technique. Similarly PSNR values are low in H.264 for all the images except Boat image when compared to the proposed technique. This result concludes that the increase in PSNR indicates that the output image has less noise as compared to existing techniques. Conclusion: The compression of the proposed algorithm is superi or to JPEG and H.264. Our new method of compression algorithm can be used to improve the performance of Compression ratio and Peak Signal to Noise Ratio (P SNR). In future this study can be extended to real time applications for video compression in medical images.

A multidimensional incremental parsing algorithm (MDIP) for multidimensional discrete sources, as a generalization of the Lempel-Ziv coding algorithm, is investigated. It consists of three essential component schemes, maximum decimation matching, hierarchical structure of multidimensional source coding, and dictionary augmentation. As a counterpart of the longest match search in the Lempel-Ziv algorithm, two classes of maximum decimation matching are studied. Also, an underlying behavior of the dictionary augmentation scheme for estimating the source statistics is examined. For an m-dimensional source, m augmentative patches are appended into the dictionary at each coding epoch, thus requiring the transmission of a substantial amount of information to the decoder. The property of the hierarchical structure of the source coding algorithm resolves this issue by successively incorporating lower dimensional coding procedures in the scheme. In regard to universal lossy source coders, we propose two distortion functions, the local average distortion and the local minimax distortion with a set of threshold levels for each source symbol. For performance evaluation, we implemented three image compression algorithms based upon the MDIP; one is lossless and the others are lossy. The lossless image compression algorithm does not perform better than the Lempel-Ziv-Welch coding, but experimentally shows efficiency in capturing the source structure. The two lossy image compression algorithms are implemented using the two distortion functions, respectively. The algorithm based on the local average distortion is efficient at minimizing the signal distortion, but the images by the one with the local minimax distortion have a good perceptual fidelity among other compression algorithms. Our insights inspire future research on feature extraction of multidimensional discrete sources.

This paper presents a new lossless image compression algorithm which uses Joint Photographic Expert Group-Lossless (JPEG-LS) prediction technique and the concept of the seed number for compression. Image is processed in blocks of size 4*4. Pixels of the blocks are placed in a table of size 16*16. The span across which the pixels of each block are spread in the table is determined which forms a rectangle. Seed number for that block is computed. Using the JPEG-LS prediction technique in regular mode, the pixels are predicted and their position in the rectangle is found. Huffman encoded seed number, rectangle position and the positions of the pixels constitute the compressed files. Image is decompressed by Huffman decoding the files and reconstructing the pixels with the help of the pixel position, rectangle position and the seed number. This approach gives a new technique to compress the image, It is a simple approach and though the compression ratio is slightly lesser compared to JPEG-LS, it can be improved by Iterative approach.

Image processing is the field of signal processing where both the input and output signals are images. Images can be thought of as two-dimensional signals via a matrix representation. Image processing a is very important subject, and finds itself in such fields as photography, satellite imaging, medical imaging, and image compression, to name but a few. In the past, image processing was largely done using analog devices (Cheng et al., 2006). However, as computers became more powerful, processing shifted toward the digital domain. Like one-dimensional digital signal processing, digital image processing overcomes traditional analog problems such as noise, distortion during processing, inflexibility of system to change, and difficulty of implementation. Image retrieval, popularly referred to as content-based image retrieval is an emerging technology that allows a user to retrieve relevant images in an effective and efficient manner. Digital imaging has extensive applications in our daily lives and it is being used for several applications. Examples of imaging applications are in museums for archiving important images and manuscripts from art gallery and museum management. Many useful applications of imaging are found in security for tracking an intruder, crime prevention, law enforcement and object recognition in digital forensic. In particular, image retrieval is potentially useful in discovering brain activation patterns, in classifications and in diagnoses by comparing observed patterns with those of known diseases, leading to clinical applications. In biomedicine, content-based image retrieval is critically important in patient digital libraries, clinical diagnosis, clinical trials, searching for 2-D electrophoresis gels, and pathological slides. Most existing content-based image retrieval systems (Flickner et al., 1995; Gupta and Jain, 1997; Ma and Manjunath, 1997; Rubner, 1999 and Wang et al., 1998) are designed for general purpose picture libraries such as photos and graph. The storage, manipulation and analysis of the contents of digital images are essential requirements for the next generation of healthcare information infrastructure. The aim of this infrastructure is to bring timely health information to support communication among healthcare decision makers and communities at large. Among several healthcare services that can be provided with the aid of the emerging grid technology for ubiquitous access, image classification and diagnosis services are important. The ubiquitous access and