Lossless compression of the geostationary imaging Fourier transform spectrometer (GIFTS) data via predictive partitioned vector quantization

As part of NASA's New Millennium Program, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) is an advanced ultraspectral sounder with a 128x128 array of interferograms for the retrieval of such geophysical parameters as atmospheric temperature, moisture, and wind. With massive data volume that would be generated by future advanced satellite sensors such as GIFTS, chances are that even the state-of-the-art channel coding (e.g. Turbo codes, LDPC) with low BER might not correct all the errors. Due to the error-sensitive ill-posed nature of the retrieval problem, lossless compression with error resilience is desired for ultraspectral sounder data downlink and rebroadcast. Previously, we proposed the fast precomputed vector quantization (FPVQ) with arithmetic coding (AC) which can produce high compression gain for ground operation. In this paper we adopt FPVQ with the reversible variable-length coding (RVLC) to provide better resilience against satellite transmission errors remaining after channel decoding. The FPVQ-RVLC method is compared with the previous FPVQ-AC method for lossless compression of the GIFTS data. The experiment shows that the FPVQ-RVLC method is a significantly better tool for rebroadcast of massive ultraspectral sounder data.

A Geostationary Imaging Fourier Transform Spectrometer (GIFTS) has been selected for the NASA New Millennium Program (NMP) Earth Observing-3 (EO-3) mission. Our paper will discuss one of the key GIFTS measurement requirements, Field of View (FOV) stability, and its impact on required system performance. The GIFTS NMP mission is designed to demonstrate new and emerging sensor and data processing technologies with the goal of making revolutionary improvements in meteorological observational capability and forecasting accuracy. The GIFTS payload is a versatile imaging FTS with programmable spectral resolution and spatial scene selection that allows radiometric accuracy and atmospheric sounding precision to be traded in near real time for area coverage. The GIFTS sensor combines high sensitivity with a massively parallel spatial data collection scheme to allow high spatial resolution measurement of the Earth's atmosphere and rapid broad area coverage. An objective of the GIFTS mission is to demonstrate the advantages of high spatial resolution (4 km ground sample distance - gsd) on temperature and water vapor retrieval by allowing sampling in broken cloud regions. This small gsd, combined with the relatively long scan time required (approximately 10 s) to collect high resolution spectra from geostationary (GEO) orbit, may require extremely good pointing control. This paper discusses the analysis of this requirement.

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.

The partitioned predictive vector quantization (PPVQ) algorithm is known for its high compression ratio for lossless compression of the ultraspectral sounder data with high spatial and spectral resolutions. With the advent of the multicore technologies, parallelization of several parts of the algorithm has been explored in previous work using a compute unified device architecture (CUDA) aided environment on the Graphics Processing Unit (GPU). Recently the Intel Many Integrated Core (MIC) architecture on a coprocessor is introduced which shows promise in handling more divergent workloads as needed in PPVQ. Therefore we will explore the parallel performance of the MIC-aided implementation. With parallelization of the two most time-consuming modules of linear prediction and vector quantization in PPVQ, the total processing time of an AIRS granule can be compressed in less than 7.5 seconds which is equivalent to a speedup of ~8.8x. The use of MIC for PPVQ compression is thus promising as a low-cost and effective compression solution for ultraspectral sounder data for ground rebroadcast use.

Lossless Data Hiding Scheme Based on VQ Index Coding

The sheer size of volume data sampled in a regular grid requires efficient lossless and lossy compression algorithms that allow for on-the-fly decompression during rendering. While all hardware assisted approaches are based on fixed bit rate block truncation coding, they suffer from degradation in regions of high variation while wasting space in homogeneous areas. On the other hand, vector quantization approaches using texture hardware achieve an even distribution of error in the entire volume at the cost of storing overlapping blocks or bricks. However, these approaches suffer from severe blocking artifacts that need to be smoothed over during rendering. In contrast to existing approaches, we propose to build a lossy compression scheme on top of a state-of-the-art lossless compression approach built on non-overlapping bricks by combining it with straight forward vector quantization. Due to efficient caching and load balancing, the rendering performance of our approach improves with the compression rate and can achieve interactive to real-time frame rates even at full HD resolution.

Image data compression is one of the most important techniques in digital image processing that used to reduce the size of image data with taking into account getting good quality of the compressed image, before several decades the researchers produced a lot of coding methods to decrease the image size, these methods include loss data compression and lossless data compression. in this research vector quantization techniques have been used to compress color image, firstly vector quantization has been applied using only cosine transform and then using principle component transform together with cosine transform, secondly vector quantization (VQ) has been achieved using wavelet transform only and then using the same transform with the principle component transform, many statistical properties have been shown and many fidelity criteria have been computed. This manuscript also includes the theory concepts of Vector Quantization, cosine transformation, principle component analysis and wavelet transform.

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.

Flash floods are among the most frequent natural disasters caused by heavy rain associated with a severe thunderstorm, which leads to social and economic losses in infrastructure and agriculture. Therefore, this research aims to map flash flood potential susceptibility (FFPS) in the Pekalen watershed, using Geographic Information System (GIS) technology and statistical analysis to reduce the risk of flooding. The opinion and experience of an expert on the weight assessment method were carried out using the Analytical Hierarchy Process (AHP). Furthermore, the probability statistical methods and GIS were used in flash flood areas in the Pekalen watershed in Andungbiru, Probolinggo village. This study was carried out using geomorphological factors, namely elevation, slope, stream power index, and topographic wetness index, with a resolution of 30 m. Thematic map scale of the land use, river density, distance to the river, rainfall, and geology is in the ratio of is in a ratio of 1:25.000. Imagery processing was carried out using Landsat 8 30 m x 30 m resolution imagery, such as the Normalized Difference Vegetation Index. The result showed that the model map of FFPS obtained low 8%, low 23%, moderate 27%, moderate to high 26%, high 13%, and very high 2% index values. The next stage of modeling analysis led to validation using statistic receiver operating Characteristic Curve (ROC) of area Under Curve (AUC) with a value of 90.15. In conclusion, the factors that significantly trigger flash floods are distance to the river, land use, and slope. Keywords: AHP-weighted; information content; FFSP; GIS; Geomorphology Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

A survey is presented of past and present systems for obtaining direct measurements of atmospheric wind, pressure, temperature, and water vapor. Estimates are included of the types of horizontal- and vertical-sounding systems which will be in field use within the next decade.

An adaptive VQ method used on interferential multi-spectral image lossless compression

We apply the cross-entropy (CE) method to problems in clustering and vector quantization. The CE algorithm for clustering involves the following iterative steps: (a) generate random clusters according to a specified parametric probability distribution, (b) update the parameters of this distribution according to the Kullback---Leibler cross-entropy. Through various numerical experiments, we demonstrate the high accuracy of the CE algorithm and show that it can generate near-optimal clusters for fairly large data sets. We compare the CE method with well-known clustering and vector quantization methods such as K-means, fuzzy K-means and linear vector quantization, and apply each method to benchmark and image analysis data.

On-board compression systems aim to increase the amount of image data transmitted to ground during a single communication session with the ground station. The adopted approach to lossless image compression employs predictive neural networks, integer wavelet transforms and Peano-Hilbert scan. The performance of these neural networks based techniques has been compared with other lossless compression algorithms such as the Rice Algorithm and JPEG2000. Testing results show that the proposed method outperforms the state-of-theart methods. The focus of the paper is on improving the speed of the neural network component of the image compression algorithm. It is shown that the number of neural networks sets and the size of image tiles affect the processing speed of the neural network predictor. A hardware implementation is proposed which is based on multiple neural processors performing compression of a certain number of image tiles in parallel. It is shown that the proposed hardware implementation can improve significantly the overall speed of the compressor.

For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.