scholarly journals Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

2019 ◽  
Vol 11 (21) ◽  
pp. 2461 ◽  
Author(s):  
Kevin Chow ◽  
Dion Tzamarias ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Hyperspectral Image ◽  
Lossless Compression ◽  
Hyperspectral Images ◽  
Direct Access ◽  
Bit Rate ◽  
Compression Algorithms ◽  
Real Time Processing ◽  
Time Processing ◽  
Differential Encoding

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

scholarly journals GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images

2020 ◽  
Vol 10 (19) ◽  
pp. 6680
Author(s):  
Chunchao Li ◽  
Yuanxi Peng ◽  
Mingrui Su ◽  
Tian Jiang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Image Data ◽  
Computer Experiments ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Real Time Processing ◽  
Time Processing

As the application of real-time requirements gradually increases or real-time processing and responding become the bottleneck of the task, parallel computing in hyperspectral image applications has also become a significant research focus. In this article, a flexible and efficient method is utilized in the noise adaptive principal component (NAPC) algorithm for feature extraction of hyperspectral images. From noise estimation to feature extraction, we deploy a complete CPU-GPU collaborative computing solution. Through the computer experiments on three traditional hyperspectral datasets, our proposed improved NAPC (INAPC) has stable superiority and provides a significant speedup compared with the OpenCV and PyTorch implementation. What’s more, we creatively establish a complete set of uncrewed aerial vehicle (UAV) photoelectric platform, including UAV, hyperspectral camera, NVIDIA Jetson Xavier, etc. Flight experimental results show, considering hyperspectral image data acquisition and transmission time, the proposed algorithm meets the feature extraction of real-time processing.

AN EFFICIENT AND SIMPLE QUAD EDGE CONVERSION OF POLYGONAL MAINFOLD OBJECTS

2001 ◽  
Vol 01 (02) ◽  
pp. 251-271
Author(s):  
KWANG-MAN OH ◽  
JEONG-DAN CHOI ◽  
CHAN-SU LEE ◽  
CHAN-JONG PARK ◽  
EE-TAEK LEE
Keyword(s):  
Data Structure ◽  
High Performance ◽  
Graphics Hardware ◽  
Delaunay Triangulations ◽  
Computer Aided Geometric Design ◽  
Real Time Processing ◽  
Time Processing ◽  
Level Of Details ◽  
Efficient Data ◽  
Processing Techniques

This paper presents an efficient and simple quad edge conversion method of polygonal (manifold) objects. In a wide variety of applications such as scientific visualization, virtual reality and computer aided geometric design, polygonal objects are expected to be visualized and manipulated within a given time constraint. To achieve these expectations, it is necessary to introduce an efficient data structure as well as high performance graphics hardware and real-time processing techniques such as simplification and level of details. The quad edge data structure is very efficient for handling polygonal objects even though it was originally designed to handle the subdivisions of manifold objects such as Delaunay triangulations and Voronoi diagrams. It, however, has not been used widely because there is no efficient algorithm for quad edge conversion of conventional polygonal objects. In this paper, we propose a new incremental quad edge conversion algorithm that processes the triangles one by one. Since quad edge has only the splice as a topological operator, the quad edge conversion of each triangle is done by applying three splice operations, a splice per vertex. As an applicaion for the quad edge, a simplification of conventional polygonal objects is implemented. It includes the removing, moving, replacing, and inserting of vertices and edges.

scholarly journals Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms

2019 ◽  
Vol 11 (19) ◽  
pp. 2290
Author(s):  
Dion Eustathios Olivier Tzamarias ◽  
Kevin Chow ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Lossless Compression ◽  
Matrix Decomposition ◽  
Hyperspectral Images ◽  
Electromagnetic Spectrum ◽  
Compression Algorithms ◽  
Integer Coefficients ◽  
Novel Technique ◽  
Spectral Graph ◽  
Large Size ◽  
Spectral Components

Hyperspectral images are depictions of scenes represented across many bands of the electromagnetic spectrum. The large size of these images as well as their unique structure requires the need for specialized data compression algorithms. The redundancies found between consecutive spectral components and within components themselves favor algorithms that exploit their particular structure. One novel technique with applications to hyperspectral compression is the use of spectral graph filterbanks such as the GraphBior transform, that leads to competitive results. Such existing graph based filterbank transforms do not yield integer coefficients, making them appropriate only for lossy image compression schemes. We propose here two integer-to-integer transforms that are used in the biorthogonal graph filterbanks for the purpose of the lossless compression of hyperspectral scenes. Firstly, by applying a Triangular Elementary Rectangular Matrix decomposition on GraphBior filters and secondly by adding rounding operations to the spectral graph lifting filters. We examine the merit of our contribution by testing its performance as a spatial transform on a corpus of hyperspectral images; and share our findings through a report and analysis of our results.

Enhanced layered segment trees: a pragmatic data structure for real-time processing of geometric objects

2002 ◽  
Vol 35 (10) ◽  
pp. 2303-2309
Author(s):  
Gopal Racherla ◽  
Sridhar Radhakrishnan ◽  
B.John Oommen
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Real Time Processing ◽  
Time Processing ◽  
Geometric Objects

scholarly journals Lossless compression of hyperspectral images with pre-byte processing and intra-bands correlation

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 166-172 ◽  
Author(s):  
Assiya Sarinova ◽  
Alexander Zamyatin ◽  
Pedro Cabral
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Compression Ratio ◽  
Remote Sensing Data ◽  
Lossless Compression ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Compression Algorithms ◽  
Auxiliary Data ◽  
Sensing Data

This paper considers an approach to the compression of hyperspectral remote sensing data by an original multistage algorithm to increase the compression ratio using auxiliary data processing with its byte representation as well as with its intra-bands correlation. A set of the experimental results for the proposed approach of effectiveness estimation and its comparison with the well-known universal and specialized compression algorithms is presented.

scholarly journals Optimization of computational complexity of lossy compression algorithms for hyperspectral images

2019 ◽  
pp. 297-301
Author(s):  
L I Lebedev ◽  
A O Shakhlan
Keyword(s):  
Computational Complexity ◽  
Hyperspectral Image ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Hyperspectral Images ◽  
Recognition Method ◽  
Compression Algorithms ◽  
Adaptive Partitioning ◽  
Image Pixels

In this paper, we consider the solution of the problem of increasing the speed of the algorithm for hyperspectral images (HSI) compression, based on recognition methods. Two methods are proposed to reduce the computational complexity of a lossy compression algorithm. The first method is based on the use of compression results obtained with other parameters, including those of the recognition method. The second method is based on adaptive partitioning of hyperspectral image pixels into clusters and calculating the estimates of similarity only with the templates of one of the subsets. Theoretical and practical estimates of the increase in the speed of the compression algorithm are obtained.

GPU Acceleration of Algorithm to Design Directional Volumetric Display for Real-time Processing

2019 ◽  
pp. 989
Author(s):  
Daiki Matsumoto ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  
Keyword(s):  
Real Time ◽  
Gpu Acceleration ◽  
Real Time Processing ◽  
Time Processing ◽  
Volumetric Display

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

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