COLOR IMAGES COMPRESSION USING VECTOR QUANTIZATION (VQ)TECHNIQUE WITH DIFFERENT MATHEMATICAL TRANSFORMATIONS

Summary form only given. Large aperture static imaging spectrometer (LASIS) is a new kind of interferometer spectrometer with the advantages of high throughput and large field of view. The LASIS data contains spatial information in principle component along spatial direction and spectral information in modulated component along Optical Path Difference (OPD) direction. LASIS data have clearly parallel displacement of spatial information among continuous frames. It is a unique characteristic for LASIS data compared with other kinds of interferometer spectrometer such as SMII data and GITFS data. Although dispersive spectrometer, such as AVIRIS data, also contains some spatial information, each frame in a group shows the same ground view with different wavelengths spectrum without any motion displacement among inter frames. Based on these characteristics, we propose a lossless data compression method named Dual-direction Prediction Vector Quantization (DPVQ). With a dual-direction prediction on both spatial and spectral direction, redundancy in LASIS data is largely removed by minimizing the prediction residue in DPVQ. Then a fast vector quantization (VQ) avoiding codebook splitting process is applied after prediction. Considering time efficiency, the dual-direction prediction and VQ in DPVQ are optimized to reduce the calculations, so that optimized prediction saves 60% running time and fast VQ saves about 25% running time with a similar quantization quality compared with classical generalized Lloyd algorithm (GLA). Experimental results, shown in Fig. 2, indicate that DPVQ can achieve a maximal Compression Ratio (CR) at about 3.4, which outperforms many existing lossless compression algorithms.

The compression of three-dimensional ultraspectral sounder data is a challenging task given its unprecedented size. We develop a fast precomputed vector quantization (FPVQ) scheme with optimal bit allocation for lossless compression of ultraspectral sounder data. The scheme consists of linear prediction, bit-depth partitioning, vector quantization, and optimal bit allocation. Linear prediction serves as a whitening tool to make the prediction residuals of each channel close to a Gaussian distribution, and then these residuals are partitioned based on bit depths. Each partition is further divided into several sub-partitions with various 2/sup k/ channels for vector quantization. Only the codebooks with 2/sup m/ codewords for 2/sup k/-dimensional normalized Gaussian distributions are precomputed. A new algorithm is developed for optimal bit allocation among subpartitions. Unlike previous algorithms that may yield a sub-optimal solution, the proposed algorithm guarantees to find the minimum of the cost function under the constraint of a given total bit rate. Numerical experiments upon the NASA AIRS data show that the FPVQ scheme gives high compression ratios for lossless compression of ultraspectral sounder data.

This paper addresses the problem of data compression of medical imagery such as X-rays, Computer Tomography, Magnetic Resonance, Nuclear Medicine and Ultrasound. The Discrete Cosine Transform (DCT) has been extensively studied for image data compression, and good compression has been obtained without unduly sacrificing image quality. Vector Quantization has only recently been applied to image data compression, but shows promise of outperforming more traditional transform coding methods, especially at high compression. Vector Quantization is quite well suited for those applications where the images to be processed are very much alike, or can be grouped into a small number of classifications. These and similar studies continue to suffer from the lack of a uniformly agreed upon measure of image quality. This is also exacerbated by the large variety of electronic displays and viewing conditions.

Digital image processing, post-processing, and data integration techniques as applied to airborne and satellite remotely sensed data for the purpose of extracting useful Earth resources information will be discussed in this chapter. Image preprocessing and data reduction tools are described in the previous chapter. The concepts discussed in this chapter include: • Image processing techniques such as unsupervised image classifications, supervised image classifications, neural network classifiers, simulated annealing classifiers, and fuzzy logic classification systems • The most widely accepted indices and land use/land cover classification schemes • Post-processing techniques such as filtering and change detection • Accuracy assessment and validation of results • Data integration and spatial modeling including examples of integration of remotely sensed data with other conventional survey and map form data for Earth observation purposes

The Geostationary Imaging Fourier Transform Spectrometer (GIFTS), as part of NASA's New Millennium Program, is an advanced instrument to provide high-temporal-resolution measurements of atmospheric temperature and water vapor, which will greatly facilitate the detection of rapid atmospheric changes associated with destructive weather events, including tornadoes, severe thunderstorms, flash floods, and hurricanes. The Committee on Earth Science and Applications from Space under the National Academy of Sciences recommended that NASA and NOAA complete the fabrication, testing, and space qualification of the GIFTS instrument and that they support the international effort to launch GIFTS by 2008. Lossless data compression is critical for the overall success of the GIFTS experiment, or any other very high data rate experiment where the data is to be disseminated to the user community in real-time and archived for scientific studies and climate assessment. In general, lossless data compression is needed for high data rate hyperspectral sounding instruments such as GIFTS for (1) transmitting the data down to the ground within the bandwidth capabilities of the satellite transmitter and ground station receiving system, (2) compressing the data at the ground station for distribution to the user community (as is traditionally performed with GOES data via satellite rebroadcast), and (3) archival of the data without loss of any information content so that it can be used in scientific studies and climate assessment for many years after the date of the measurements. In this paper we study lossless compression of GIFTS data that has been collected as part of the calibration or ground based tests that were conducted in 2006. The predictive partitioned vector quantization (PPVQ) is investigated for higher lossless compression performance. PPVQ consists of linear prediction, channel partitioning and vector quantization. It yields an average compression ratio of 4.65 on the GIFTS test data, which significantly outperforms the standard compression methods such as JPEG-2000, JPEG-LS, and CCSDS IDC 9/7M & 5/3.

Neste trabalho, três diferentes técnicas para medir e monitorar a abertura de fissura e a posição da linha neutra são investigadas, sendo elas: strain gauges, análise digital de imagens e transdutores de deslocamento. Foram ensaiadas à flexão vigas de concreto reforçado com fibras de aço, nas quais foram monitoradas as cargas, os deslocamentos, as deformações à compressão, a abertura de fissura e a posição da linha neutra. Para as técnicas dos transdutores de deslocamento e da análise digital de imagens são apresentadas formulações matemáticas que devem ser empregadas para a correção dos valores de abertura de fissura e posição da linha neutra em função do deslocamento e do giro da viga durante o ensaio de flexão. Os resultados mostraram que as técnicas do strain gauges e dos transdutores de deslocamento foram mais eficientes para monitorar a posição da linha neutra. Na técnica da análise digital de imagens observou-se que um pequeno erro na medição das deformações nos pontos da grade de referência colada na lateral da viga ocasionou um grande erro no valor da posição da linha neutra medido. A abertura de fissura só foi possível ser medida por meio das técnicas dos transdutores de deslocamento e da análise digital de imagens, sendo que ambas apresentaram bons resultados. Verificou-se que a melhor técnica para monitorar tanto a posição da linha neutra quanto a abertura de fissura foi a dos transdutores de deslocamento, além de ser a técnica que apresenta maior facilidade de aplicação.

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

The present report is devoted to the study of distinct ionization waves in terms of temporally-, spatially-, and wavelength-resolved analyses. The study is based on the technique of two-dimension fast imaging. However, appropriately selected ultraviolet to near infrared optical filters are employed to capture the propagation of specific species and digital image processing techniques are applied to explore the recorded snapshots. N2(SPS), (FNS), He I, OH(A–X), and O I, i.e., emissive neutral and ionic species, are investigated. On the other hand, the propagation of the NO γ species is studied by means of laser-induced fluorescence spectroscopy, since the light intensity due to spontaneous emission of this species was not readily detectable. Digital image processing techniques are also applied for the NO γ case. The crucial role of the above species in fields like plasma biomedicine and material processing is extensively recognized, and the present work: (i) provides gathered information on the propagation of these species within the atmospheric air; and (ii) introduces image processing algorithms to extract information which otherwise would remain hidden or uncorrelated with other plasma parameters. The present results unveil specific emission patterns due to the propagation of the N2(SPS), (FNS), He I, OH(A–X), and O I species. The intensity patterns consist of a first peak located in the vicinity of the reactor orifice, a second peak moving away from the reactor orifice, and a continuum which couples the two peaks. However, the detailed features of each pattern depend on the type of species considered. The fluorescence of the NO γ species also suggests the division of the area downstream of the reactor orifice into two regions where distinct ionization-excitation effects take place. Propagation speeds of the species up to about 2 × 105 m s−1 were measured. Finally, qualitative correlation between the species propagation (as it is pronounced by the emission patterns) and the local electric field (as it was numerically calculated in a similar setup) is demonstrated.

Polynomial IIR digital filters play a crucial role in the process of image data compression. The main purpose of designing polynomial IIR digital filters of the integer parameters space and introduce efficient filters to compress image data using a singular value decomposition algorithm. The proposed work is designed to break down the complex topic into bite-sized pieces of image data compression through the lens of compression image data using Infinite Impulse Response Filters. The frequency response of the filters is measured using a real signal with an automated panoramic measuring system developed in the virtual instrument environment. The analysis of the output signal showed that there are no limit cycles with a maximum radius of poles of 0.96 in the polynomial bandpass filters. Thus, all the functional requirements for the Integer Parameters Space of the proposed polynomial IIR digital filters were met. The results showed that the data compression and size reducing of an image file is processed without significantly impacting of visual quality. This is achieved by removing redundant or unnecessary information from the image while preserving the important details which removes unnecessary data to make the file smaller and more manageable.

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

Versatile image processing technique for fuel science: A review.

We use the term data to mean digital data: data that is represented as a sequence of characters drawn from the input alphabet ∑. Data compression is the process of encoding (“compressing”) a body of data into a smaller body of data. With lossless data compression, it must be possible for the compressed data to be decoded (“decompressed”) back to the original data, whereas with lossy data compression, compressed data need only be decoded back to some acceptable approximation to the original data (where “acceptable” is defined by some fidelity criteria that is provided to the algorithm).

Image data compression is essential for a number of applications that involve transmission and storage. One technique that has been recently extensively investigated is vector quantization (VQ). For image sequence coding, we usually need adaptive algorithms to exploit the high correlation between successive frames. In this paper, we describe an adaptive technique for image sequence coding based on vector quantization. The algorithm is called Variable Size And Code (VSAC), and it has a codebook that varies both in size and code as successive frames are encoded to closely match the local statistics of the current frame. Experimental results are presented on a test sequence and demonstrate that the proposed technique is efficient and that it maintains a nearly constant distortion over the entire sequence.

Image data compression is essential for a number of applications that involve transmission and storage. One technique that has been recently extensively investigated is vector quantization (VQ). One class of neural networks (NN) structures, namely competitive learning networks appears to be particularly suited for VQ. One main feature that characterizes NN training algorithms is that the VQ codewords are obtained in an adaptive manner. In this paper, a new competitive learning (CL) algorithm called the Threshold Competitive Learning (TCL) is introduced. The algorithm uses a threshold to determine the codewords to be updated after the presentation of each input vector. The threshold can be made variable as the training proceeds and more than one threshold can be used. The new algorithm can be easily combined with other NN training algorithms such as the Frequency-Sensitive competitive learning (FSCL) or the Kohonen Self-Organizing Feature Maps (KSFM). The new algorithm is shown to be efficient and yields results comparable to the famous traditional LBG algorithm.

The decreasing cost of computing power and the introduction of low cost imaging boards justifies the increasing number of applications of digital image processing techniques in the area of biomedicine. There is however a large software gap to be fulfilled, between the application and the equipment. The requirements to bridge this gap are twofold: good knowledge of the hardware provided and its interface to the host computer, and expertise in digital image processing and analysis techniques. A software package incorporating these two requirements was developped using the C programming language, in order to create a user friendly image processing programming environment. The software package can be considered in two different ways: as a data structure adapted to image processing and analysis, which acts as the backbone and the standard of communication for all the software; and as a set of routines implementing the basic algorithms used in image processing and analysis. Hardware dependency is restricted to a single module upon which all hardware calls are based. The data structure that was built has four main features: hierchical, open, object oriented, and object dependent dimensions. Considering the vast amount of memory needed by imaging applications and the memory available in small imaging systems, an effective image memory management scheme was implemented. This software package is being used for more than one and a half years by users with different applications. It proved to be an efficient tool for helping people to get adapted into the system, and for standardizing and exchanging software, yet preserving flexibility allowing for users' specific implementations. The philosophy of the software package is discussed and the data structure that was built is described in detail.