Efficient Compression Method Research Articles

A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation

Remote sensing sensors generate useful information about climate and the Earth's surface, and are widely used in resource management, agriculture, and environmental monitoring. Compression of the RS data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as from remote sensing. In this paper, a less complex and efficient lossless compression method for images is introduced. It is based on improving the energy compaction ability of prediction models. The proposed method is applied to image processing, RS grey scale images, LiDAR rasterized data, and hyperspectral images. All the results are evaluated and compared with different lossless JPEG and a lossless version of JPEG2000, thus confirming that the proposed lossless compression method leads to a high speed transmission system because of a good compression ratio and simplicity.

A NOVEL MULTIDICTIONARY BASED TEXT COMPRESSION

The amount of digital contents grows at a faster speed as a result does the demand for communicate them. On the other hand, the amount of storage and bandwidth increases at a slower rate. Thus powerful and efficient compression methods are required. The repetition of words and phrases cause the reordered text much more compressible than the original text. On the whole system is fast and achieves close to the best result on the test files. In this study a novel fast dictionary based text compression technique MBRH (Multidictionary with burrows wheeler transforms, Run length coding and Huffman coding) is proposed for the purpose of obtaining improved performance on various document sizes. MBRH algorithm comprises of two stages, the first stage is concerned with the conversion of input text into dictionary based compression .The second stage deals mainly with reduction of the redundancy in multidictionary based compression by using BWT, RLE and Huffman coding. Bib test files of input size of 111, 261 bytes achieves compression ratio of 0.192, bit rate of 1.538 and high speed using MBRH algorithm. The algorithm has attained a good compression ratio, reduction of bit rate and the increase in execution speed.

Adaptive efficient compression of genomes

Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. However, memory requirements of the current algorithms are high and run times often are slow. In this paper, we propose an adaptive, parallel and highly efficient referential sequence compression method which allows fine-tuning of the trade-off between required memory and compression speed. When using 12 MB of memory, our method is for human genomes on-par with the best previous algorithms in terms of compression ratio (400:1) and compression speed. In contrast, it compresses a complete human genome in just 11 seconds when provided with 9 GB of main memory, which is almost three times faster than the best competitor while using less main memory.

Lossless Compression Method for Medical Image Sequences Using Super-Spatial Structure Prediction and Inter-frame Coding

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.

DELIMINATE—a fast and efficient method for loss-less compression of genomic sequences

An unprecedented quantity of genome sequence data is currently being generated using next-generation sequencing platforms. This has necessitated the development of novel bioinformatics approaches and algorithms that not only facilitate a meaningful analysis of these data but also aid in efficient compression, storage, retrieval and transmission of huge volumes of the generated data. We present a novel compression algorithm (DELIMINATE) that can rapidly compress genomic sequence data in a loss-less fashion. Validation results indicate relatively higher compression efficiency of DELIMINATE when compared with popular general purpose compression algorithms, namely, gzip, bzip2 and lzma. Linux, Windows and Mac implementations (both 32 and 64-bit) of DELIMINATE are freely available for download at: http://metagenomics.atc.tcs.com/compression/DELIMINATE. sharmila@atc.tcs.com Supplementary data are available at Bioinformatics online.

Decomposition and compression for ECG and EEG signals with sequence index coding method based on matching pursuit

An efficient compression method is proposed by encoding the sequence index of atoms based on matching pursuit (MP) algorithm with over-complete Gabor dictionary, which has the merit to adjust the compression ratio (CR) according to the practical request with low distortion. It is also combined with genetic algorithm (GA) to reduce the computation complexity. Then, the validity of this method is verified by applying it in the compression of electrocardiography (ECG) and Electroencephalography (EEG) signals. The simulation results show that the CR can be achieved at 18:1 with only 1.06% and 2.15% reconstruction errors on ECG and EEG signals respectively. It has higher CR and less reconstruction errors compared to that of the traditional methods, and noise suppression effect is also presented.

Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and Tucker Decomposition (DWT-TD), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and TD to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance of the proposed method. Moreover, we show the impact of compression HSIs on the supervised classification and linear unmixing.

Enhanced Hybrid Compound Image Compression Algorithm Combining Block and Layer-based Segmentation

This paper presents an efficient compound image compression method based on both block and layer– based segmentation techniques, which introduces a new hybrid scheme for segmenting compound images. In this hybrid model the image is first segmented into five different blocks using block based classification again the overlapping block is segmented using layer based segmentation for improving the compression ratio, visual quality.

A Novel Real Time Algorithm for Remote Sensing Lossless Data Compression based on Enhanced DPCM

In this paper, simplicity of prediction models for image transformation is used to introduce a low complex and efficient lossless compression method for LiDAR rasterized data and RS grayscale images based on improving the energy compaction ability of prediction models. Further, proposed method is applied on some RS images and LiDAR test cases, and the results are evaluated and compared with other compression methods such as lossless JPEG and lossless version of JPEG2000. Results indicate that the proposed lossless compression method causes the high speed transmission system because of good compression ratio and simplicity and suggest to use in real time processing.

A High Efficient Compression Method for Power Quality Applications

This paper introduces a high efficient compression method for power quality (PQ) signals. The proposed method makes use of the interpolated discrete Fourier transform algorithm for estimating the parameters (amplitude, frequency, and phase) of the fundamental and harmonic components and separates them from the transient ones in the original PQ signal. When these transient components are submitted to the compression technique, the sparse representation property of the wavelet transform (WT) provides an improvement in the compression ratio (CR). The experimental results show that the proposed method is suitable for various types of PQ signals and that it achieves a higher CR compared to the traditional WT-based compression techniques.

Efficient method for lossless LIDAR data compression

Light Detection and Ranging (LIDAR) has become one of the prime technologies for rapid collection of vast spatial data, usually stored in a LAS file format (LIDAR data exchange format standard). In this article, a new method for lossless LIDAR LAS file compression is presented. The method applies three consequent steps: a predictive coding, a variable-length coding and an arithmetic coding. The key to the method is the prediction schema, where four different predictors are used: three predictors for x, y and z coordinates and a predictor for scalar values, associated with each LIDAR point. The method has been compared with the popular general-purpose methods and with a method developed specially for compressing LAS files. The proposed method turns out to be the most efficient in all test cases. On average, the LAS file is losslessly compressed to 12% of its original size.

An Improved Linear Difference Method with High ROM Compression Ratio in Direct Digital Frequency Synthesizer

The increasing demand of low power Direct Digital Frequency Synthesizer (DDFS) leads to the requirement of efficient compression methods to reduce ROM size for storing sine function values. This paper presents a technique to achieve very high compression ratio by using the optimized four-segment linear difference method. The proposed technique results in the ROM compression ratio of about 117.3: 1 and the word size reduction of 6bits for the design of a DDFS with 11-bit sine amplitude output. This high compression ratio result is very promising to meet the requirement of low power consumption and low hardware complexity in digital VLSI technology.

A new code compression method for FOTA

This paper presents a new compression method for compressed Firmware-over-the-air (FOTA) that reconstructs a new compressed version from an old compressed version of firmware stored in NAND flash memory. The compressed FOTA requires a small delta that minimizes the amount of update data and an efficient compression method that provides fast encoding and decoding to achieve fast updating and extraction. A fast dictionary-based compression algorithm for the program code is introduced. With a high compression ratio, our method provides relatively fast encoding and very fast decoding. We demonstrate that the compression time during the update process can be reduced by utilizing the copy information stored in the delta without increasing the size of the delta. We present some experimental results to show the impact of this method in terms of the compression ratio, extraction time and updating time.

Seg Y Format Header Identification Data Lossless Prediction Coding

AbstractIn this paper, the header ratio is defined as the ratio between the SEG Y header data volume and the SEG Y file size, a formula describing the relationship between the header ratio, the compression ratio of SEG Y header data and the compression ratio of the SEG Y file is derived. It is discovered from the formula that the theoretical limit of the SEG Y file compression ratio is the quotient of the SEG Y header data compression ratio divided by the header ratio, therefore, it is necessary to compress the header data efficiently in order to get a high compression of the SEG Y file. Furthermore, the statistical properties of the SEG Y reel header data and trace header data are analyzed. An efficient lossless compression method for SEG Y header data, known as Header Identification Data Lossless Prediction Coding (HIDLPC) method, is proposed based on the statistical properties of the SEG Y header data. Experimental results show that the lossless compression ratio by using HIDLPC to compress SEG Y header data locates between 30 and 1000, much higher than the corresponding compression ratios by using Winzip and WinRAR.

Enhanced three-dimensional discrete cosine transform based compression method for integral images by adaptive three-dimensional block construction

In this paper, we propose an efficient compression method for integral images based on three-dimensional discrete cosine transform (3D-DCT). Even though the existing 3D-DCT based techniques are efficient, they may not be optimized to the characteristics of integral images, such as applying a fixed size block construction and a fixed scanning in placing 2D blocks to construct a 3D block. Therefore, we propose a variable size block construction and a scanning method adaptive to characteristics of integral images, which are realized by adaptive 3D block modes. Experimental results show that the proposed method gives significant improvement in coding efficiency. In particular, at the high bit rates, the proposed method is more improved, since overhead bits for signaling of the 3D block modes take a smaller part of the total bits.

A fast near-optimum block truncation coding method using a truncated K-means algorithm and inter-block correlation

AbstractBlock truncation coding (BTC) is an efficient image compression method which finds application in diverse fields. The basic problem can be viewed as a two-level quantization process. However, efficient ways for optimal threshold determination have not been discovered so far. We propose a fast BTC algorithm based on a truncated K-means algorithm, utilizing the image inter-block correlation and the fact that K-means algorithm converges very fast in this case. This produces near-optimum solution with significantly improved speed over other methods. Simulation results confirm such advantages.

A new method for video data compression by quadratic Bézier curve fitting

This paper presents a new and efficient method for video data compression using quadratic Bezier curve fitting. The method treats the luminance or color variations of a spatial location in a sequence of frames as input points in Euclidean space R 1 or R 3. The input points are approximated using quadratic Bezier least square fitting. The output data consists of quadratic Bezier control points and difference between original and fitted data. Video data compression is the main application of proposed method. It is shown that entropy of output data is significantly less than the entropy of input data. The method can be applied to 1-D space like luminance and chrominance components separately or 3-D color spaces such as RGB and YC b C r .

SoC Test Data Compression Technique Based on RLE-G

Test data compression has been an effective way to reduce test data volume and test time, as well as to solve automatic test equipment (ATE) memory and bandwidth limitation. We analyze the limitations of current test data compression algorithm and draw on the previous experience to deduce an optimal compression coding model suitable for SoC test data. In addition, in this paper we make full use of the relevance of the test vectors and the advantages of statistical coding to present an efficient test data compression method RLE-G based on the coding model, and give the RLE-G the optimal compression efficiency of the boundary conditions and realization steps. The experimental results for ISCAS 89 benchmark circuits demonstrate RLE-G have the excellent advantages of high compression ratio.

Low-bit-rate coding of underwater color image using improved wavelet difference reduction

Underwater image compression has been the key technology for transmitting massive amount of image data via underwater acoustic channel with limited bandwidth. According to the characteristics of underwater color images, an efficient underwater image compression method has been developed. The new coding scheme employs a wavelet-based preprocessing method to remove the visual redundancy, and adopts a Wavelet Tree-based Wavelet Difference Reduction (WTWDR) algorithm to remove the spatial redundancy of underwater color images. Instead of scanning whole transformed image like the WDR method, the difference reduction coding is used for each significant wavelet tree in the proposed WTWDR algorithm based on the correlation between the subbands of higher levels and lower levels of a transformed image. The experimental results show that for underwater color images the proposed method outperforms both WDR and SPIHT at very low bit rates in terms of compression ratio and reconstructed quality, while for natural images it has similar performance with WDR and SPIHT. Hence, the proposed approach is especially suitable for underwater color image compression at very low bit rates.

An efficient clustering method for fast rendering of time-varying volumetric medical data

Visualization and exploration of time-varying volumetric medical data help clinicians for better diagnosis and treatment. However, it is a challenge to render these data in an interactive manner because of their complexity and large size. We propose an efficient clustering method for fast compression and rendering of these large dynamic data. We divide the volumes into a set of blocks and use a BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) based algorithm to cluster them, which can usually find a high quality clustering with a single scan of the blocks. In addition, the granularity of clusters can be adaptively adjusted by dynamically configuring threshold values. In each cluster of blocks, a KeyBlock is generated to represent the cluster, and therefore the storage space of the volumes is reduced greatly. In addition, we assign a lifetime to every KeyBlock and implement a dynamic memory management scheme to further reduce the storage space. During the rendering, each KeyBlock is rendered as a KeyImage, which can be reused if the view transformation and transfer function are not changed. This reuse can help to increase the rendering speed significantly. Experimental results showed that the proposed method can achieve good performance in terms of both speed optimization and space reduction.