A bitmask-based code compression technique for embedded systems

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.

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.

Edge preservance and block effect reduction by block coefficient diffusion method

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.

Magnetic resonance imaging (MRI) and computer tomography (CT) are very important techniques that are used in disease diagnosis in medicine. In an average sized hospital, many tera-bytes of digital imaging data (MRI and CT) are generated every year, almost all of which has to be kept and archived. The compression of medical images is currently performed by using different algorithms. The most common compression technique is vector quantization. Interframe coding, the discrete Hartley transform, mixed transform, entropy-coded DPCM and JPEG algorithm are also used. Hierarchical finite-state vector quantization (HFSVQ), which is the improved version of vector quantization, has been introduced as a compression technique with high compression ratios for video images. Although the HFSVQ algorithm is the most efficient compression technique according to its compression ratios, we show that it has also a good performance for brain tomography and magnetic resonance images.

In embedded system design, memory is one of the most restricted resources, posing serious constraints on program size. Code compression has been used as a solution to reduce the code size for embedded systems. Lossless data compression techniques are used to compress instructions, which are then decompressed on-the-fly during execution. Previous work used fixed-to-variable coding algorithms that translate fixed-length bit sequences into variable-length bit sequences. In this paper, we present a class of code compression techniques called variable-to-fixed code compression (V2FCC), which uses variable-to-fixed coding schemes based on either Tunstall coding or arithmetic coding. Though the techniques are suitable for both reduced instruction set computer (RISC) and very long instruction word (VLIW) architectures, they favor VLIW architectures which require a high-bandwidth instruction prefetch mechanism to supply multiple operations per cycle, and fast decompression is critical to overcome the communication bottleneck between memory and CPU. Experimental results for a VLIW embedded processor TMS320C6x show that the compression ratios using memoryless V2FCC and Markov V2FCC are around 82.5% and 70%, respectively. Decompression unit designs for memoryless V2FCC and Markov V2FCC are implemented in TSMC 0.25-/spl mu/m technology.

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.

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...

We address the problem of applying the Re-Pair [LM99] algorithm to large texts. The Re-Pair algorithm (Re-Pair) proposed by Larsson and Moffat in 1999 is a simple grammar-based compression method that achieves an extremely good compression ratio. However, Re-Pair is not applicable to a large text because it consumes much memory and runs in an offline manner. Dividing the input into a consecutive sequence of smaller blocks works well for the problem, but this solution makes the compression ratio worse. In our previous research we proposed a method that shares a part of dictionaries of Re-Pair among blocks in order to reduce the decrease, where the shared dictionary is constructed at the beginning. In this paper we present a new method that adaptively reconstructs the shared dictionary. We implemented our method and investigated its performance through several experiments. The results show that our method runs much faster than an existent method and obtains a good compression ratio comparable to well-known compression tools.

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.

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose Camel , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable. Camel compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value, Camel identifies values that enable higher compression ratios. Camel also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of Camel and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that Camel is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.

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.

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).

Efficient and accurate waveform compression is essentially important for the application of analog circuit transition simulation nowadays. In this article, an analog waveform compression scheme is proposed which includes two compressed formats for representing small and large signal values, respectively. The compressed formats and the corresponding compression/decompression approaches ensure that both the absolute and relative errors of each signal value restored from the compression are within specified criteria. The formats are integrated in a block-by-block compressing procedure which facilitates a secondary lossless compression and a three-stage pipeline scheme for fast conversion from the simulator's output to the compressed hard-disk file. Theoretical analysis is presented to prove the accuracy-ensured property of our approach. Two schemes are also proposed to incorporate the prediction method. They achieve larger compression ratio for some cases while preserving the accuracy and runtime efficiency. Experiments are carried out with voltage waveforms from industrial circuits. The results validate the accuracy and the efficiency of the proposed techniques. The obtained compression ratio is 2.6X larger than existing work without overhead, even though the latter induces much larger error. Compared with the original double-precision floating number format, the proposed approach achieves the compression ratio of 26 averagely and up to 70, while keeping the relative error less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and absolute error less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> . And with the pipelined computation, the proposed compression approach hardly increases time cost to the transient simulation.