Adaptive Dictionary Sharing Method for Re-Pair Algorithm

Images include information about human body which is used for different purpose such as medical examination security and other plans Compression of images is used in some applications such as profiling information and transmission systems. Regard to importance of images information, lossless or loss compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal to noise ratio and minimum mean square error. In real time application which needs hardware implementation, low complex algorithm accelerate compression process. In this paper, we use differential pulse code modulation for image compression lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. This method is consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introducing this compression method it is applied on different images from Corel dataset for experimental results and analysis. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio and mean square error.

The Re-Pair algorithm (Re-Pair), proposed by Larsson and Moat, is a simple grammar-based compression method that achieves a good compression ratio. Although Re-Pair runs in O(n) time and space for an input of length n, it cannot be used with a large input, because it runs oine and consumes substantial memory space. In this paper, we propose an online grammar transformation algorithm based on a modied Re-Pair along with a compression method using the algorithm. The proposed algorithm runs in O(n log^ h) time using O(g) space, where g and ^h are the number of production rules in a grammar and the maximum height of syntax trees generated by the rules, respectively. We implemented our method and demonstrated that it signicantly reduces memory usage with little sacrice of compression ratio in comparison with the original Re-Pair.

A simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra. The study discusses the fundamental aspects of spectral correlation, distributions, and noise; the correlation between principal components (PCs) and atmospheric-level temperature and water vapor; and how an optimal subset of PCs is selected so a good compression ratio and high retrieval accuracy are obtained. Principal component analysis, principal component compression, and principal component regression under certain conditions are shown to provide 1) nearly full spectral information with little degradation, 2) noise reduction, 3) data compression with a compression ratio of approximately 15, and 4) tolerable loss of accuracy in temperature and water vapor retrieval. The techniques will therefore be valuable tools for data compression and the accurate retrieval of meteorological parameters from new-g...

Embedded systems are constrained by the available memory. Code compression techniques address this issue by reducing the code size of application programs. Dictionary-based code compression techniques are popular because they offer both good compression ratio and fast decompression scheme. Recently proposed techniques by J. Prakash et al. (2003) improve standard dictionary-based compression by considering mismatches. This paper makes two important contributions: i) it provides a cost-benefit analysis framework for improving the compression ratio by creating more matching patterns, and ii) it develops an efficient code compression technique using bitmasks to improve the compression ratio without introducing any decompression penalty. To demonstrate the usefulness of our approach we have used applications from various domains and compiled for a wide variety of architectures. Our approach outperforms the existing dictionary-based techniques by an average of 15%, giving a compression ratio of 55% - 65%

The multiple array technique proposed in this paper is a lossless compression technique that involves no transformation, and achieves a very good compression ratio. This technique is based on forming three arrays to indicate and code the changes in the adjacent pixels. The arrays thus formed can be further compressed using Huffman coding. The computational complexity is greatly reduced thus producing faster compressions and decompressions. Due to the combination of simplicity and compression potential of this technique, the algorithm "enjoys the best of both worlds". This technique attains compression ratios similar to or superior than the lossless image compression standards presently available. Due to the high compression ratio, the average bits required to represent a pixel is greatly reduced. Thus, this algorithm is optimally suited for telemedicine having fast transmission rates.

Thin-client computing is uniquely suitable for mobile environments, where resource-poor devices may need to access critical applications over wireless networks. In thin-client computing, applications run on a powerful server, which sends screen updates to the client in real time. However, fast and efficient coding methods for screen updates of multimedia applications are very challenging in wireless environments, for the limited bandwidth and the real-time constraint. In this paper, we present SPEDIC (Simple Predictor and Edge Detector based Image Codec), a fast and efficient codec of continuous-tone images, which is appropriate for multimedia applications in wireless thin-client computing. SPEDIC adopts predictive coding, edge coding and run coding, depending on the characteristics of image blocks, and trades off between compression ratio and coding complexity. The experimental results show that SPEDIC provides good compression ratio with very low coding complexity, especially for continuous-tone images, and achieves the best end-to-end latency over wireless networks. Compared with JPEG-LS, the standard of lossless image compression, SPEDIC compresses 2.5 times faster and decompresses 3.2 times faster than it does for a series of continuous-tone images, and is only 6.2% inferior to it in compression ratio.

Currently, much work has been done in the area of image storage and retrieval. However, the overall performance has been far from practical. A highly integrated wavelet-based image management system is proposed in this paper. By integrating wavelet-based solutions for image compression and decompression, content-based retrieval and progressive transmission, much higher performance can be achieved. The multiresolution nature of the wavelet transform has been proven to be a powerful tool to represent images. The wavelet transform decomposes the image into a set of subimages with different resolutions. From here three solutions for key aspects of image management are reached. The content-based image retrieval (CBIR) features of our system include the color, contour, texture, sample, keyword and topic information of images. The first four features can be naturally extracted from the wavelet transform coefficients. By scoring the similarity of users' requests with images in the database, those who have higher scores are noted and the user receives feedback. Image compression and decompression. Assuming that details at high resolution and diagonal directions are less visible to the human eye, a good compression ratio can be achieved. In each subimage, the wavelet coefficients are vector quantized (VQ), using the LGB algorithm, which is improved in our approach to accelerate the process. Higher compression ratio can be achieved with DPCM and entropy coding method applied together. With YIQ representation, color images can also be effectively compressed. There is a very low load on the network bandwidth by transmitting compressed image data across the network. Progressive transmission is possible by employment of the multiresolution nature of the wavelet, which makes the system respond faster and the user-interface more friendly. The system shows a high overall performance by exploring the excellent features of wavelet, and integrating key aspects of image management. An image retrieval service using Java is also available on the world wide web (www), to demonstrate the system's features.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image processing domain requires high calculation capacities and storage facilities especially when we manipulating huge images like ultrasonic and satellite ones. In fact, manipulating such images should be done without losing any kind of information. For this purpose, those images are generally used and manipulated without compression because actual compression algorithms do not offer the possibility of giving a good lossless compression ratio to override their slowliness. Lately, a new compression Standard, namely JPEG2000, was introduced. This standard offers, in addition to many interesting characteristics, the possibility of lossless image compression, under the same core and with extremely competitive compression ratio. In this paper we will describe the gridification and implementation of the JEPG2000 standard on the EUMEDGRID infrastructure. This gridified version of the Jasper implementation of the JPEG2000 standard follows three steps: each Worker Node extracts, remotely from the Storage Element (SE) based on Grid File Access Library routines (GFAL), a portion of the considered image, compresses it and saves the result back into the SE. A merge task is performed after the gathering of all image portions. We will demonstrate that, this gridified algorithm, not only, compresses gigabyte images successfully but also efficiently and in a highly competitive processing time.

Edge computing is currently one of the main research topics in the field of Internet of Things. Edge computing requires lightweight and computationally simple algorithms for sensor data analytics. Sensing edge devices are often battery powered and have a wireless connection. In designing edge devices the energy efficiency needs to be taken into account. Pre-processing the data locally in the edge device reduces the amount of data and thus decreases the energy consumption of wireless data transmission. Sensor data compression algorithms presented in this paper are mainly based on data linearity. Microclimate data is near linear in short time window and thus simple linear approximation based compression algorithms can achieve rather good compression ratios with low computational complexity. Using these kind of simple compression algorithms can significantly improve the battery and thus the edge device lifetime. In this paper linear approximation based compression algorithms are tested to compress microclimate data.

One of the most successful natural language compression methods is word-based Huffman. However, such a two-pass semi-static compressor is not well suited to many interesting real-time transmission scenarios. A one-pass adaptive variant of Huffman exists, but it is character-oriented and rather complex. In this paper we implement word-based adaptive Huffman compression, showing that it obtains very competitive compression ratios. Then, we show how End-Tagged Dense Code, an alternative to word-based Huffman, can be turned into a faster and much simpler adaptive compression method which obtains almost the same compression ratios.

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

For image compression, it is very necessary that the selection of transform should reduce the size of the resultant data as compared to the original data set. In this paper, a new lossless image compression method is proposed. For continuous and discrete time cases, wavelet transform and wavelet packet transform has emerged as popular techniques. While integer wavelet using the lifting scheme significantly reduces the computation time, we propose a completely new approach for further speeding up the computation. First, wavelet packet transform (WPT) and lifting scheme (LS) are described. Then an application of the LS to WPT is presented which leads to the generation of integer wavelet packet transform (IWPT). The proposed method, Integer Wavelet Packet Transform (IWPT) yields a representation which can be lossless, as it maps an integer valued sequence onto the integer valued coefficients. The idea of Wavelet Packet Tree is used to transform the still and color images. IWPT tree can be built by iterating the single wavelet decomposition step on both the low-pass and high-pass branches, with rounding off in order to achieve the integer transforms. Thus, the proposed method provides good compression ratio.

A low-complexity wavelet-based video coding technique is proposed which adopts JPEG-2000 image coding. The wavelet transformation and subband quantization are developed and optimized in order to reduce the ringing artifacts especially at very low bit rate. The proposed coding technique reduces the coding complexity and performs well at average PSNR. As compared to other rival coding methods the proposed coding policy has a good compression ratio and an improved visual quality.

Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data into single-node or distributed main memory to enable fast matrix-vector operations. General-purpose compression struggles to achieve both good compression ratios and fast decompression for block-wise uncompressed operations. Therefore, we introduce Compressed Linear Algebra (CLA) for lossless matrix compression. CLA encodes matrices with lightweight, value-based compression techniques and executes linear algebra operations directly on the compressed representations. We contribute effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Our experiments show good compression ratios and operations performance close to the uncompressed case, which enables fitting larger datasets into available memory. We thereby obtain significant end-to-end performance improvements.

Joint image compression and encryption using a novel column-wise scanning and optimization algorithm