5 research outputs found
ΠΠ«Π―ΠΠΠΠΠΠ Π ΠΠΠΠΠ§ΠΠ Π ΠΠΠΠ’ΠΠ ΠΠ«Π₯ ΠΠΠΠΠ«Π₯ ΠΠ Π Π’ΠΠΠΠ’ΠΠ§ΠΠ‘ΠΠΠ ΠΠΠ ΠΠΠΠ’ΠΠ ΠΠΠ‘ΠΠΠ§ΠΠ‘ΠΠΠ₯ Π‘ΠΠΠΠΠΠ
The problem of differences identifying in vector graphics data packages and how to solve it is considered. Map information and vectorized data of remote sensing of Earth are sources of vector data in the article. The aim is to design a method for detecting differences in vector data packages, providing reliable results for updating map and monitoring areas tasks. Research and development is done by mathematical modeling of the task in MATLAB. The article provides developed method for solving the task and results of its application for finding differences between two vector data packages, obtained from the target information of remote sensing of the Earth, and/or vector layers of digital district map. This method allows you to automate the process and reduce the time of thematic analysis of cosmic information, obtained from remote sensing of the Earth for topographic mapping and monitoring areas.Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠΈΠΉ Π² ΠΏΠ°ΠΊΠ΅ΡΠ°Ρ
Π΄Π°Π½Π½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉ Π³ΡΠ°ΡΠΈΠΊΠΈ ΠΈ ΡΠΏΠΎΡΠΎΠ±Ρ Π΅Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ. ΠΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
Π² ΡΡΠ°ΡΡΠ΅ ΡΠ»ΡΠΆΠ°Ρ ΠΊΠ°ΡΡΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΈ Π²Π΅ΠΊΡΠΎΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠ΅ΠΌΠ»ΠΈ. Π¦Π΅Π»ΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΏΠΎΡΠΎΠ±Π° Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠΈΠΉ Π² ΠΏΠ°ΠΊΠ΅ΡΠ°Ρ
Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠ΅Π³ΠΎ Π½Π°Π΄ΡΠΆΠ½ΠΎΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ° Π΄Π»Ρ Π·Π°Π΄Π°Ρ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΊΠ°ΡΡΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΌΠ΅ΡΡΠ½ΠΎΡΡΠΈ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ ΠΏΡΡΡΠΌ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ Π² MATLAB. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π΄Π»Ρ Π½Π°Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠΈΠΉ ΠΌΠ΅ΠΆΠ΄Ρ Π΄Π²ΡΠΌΡ ΠΏΠ°ΠΊΠ΅ΡΠ°ΠΌΠΈ Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΌΠΈ ΠΈΠ· ΡΠ΅Π»Π΅Π²ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠ΅ΠΌΠ»ΠΈ ΠΈ/ΠΈΠ»ΠΈ Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ
ΡΠ»ΠΎΡΠ² ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ°ΡΡΡ ΠΌΠ΅ΡΡΠ½ΠΎΡΡΠΈ. ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΈ ΡΠΎΠΊΡΠ°ΡΠΈΡΡ Π²ΡΠ΅ΠΌΡ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΠΎΠΉ ΠΎΡ ΡΡΠ΅Π΄ΡΡΠ² Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠ΅ΠΌΠ»ΠΈ Π΄Π»Ρ ΡΠΎΠΏΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ°ΡΡΠΎΠ³ΡΠ°ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΌΠ΅ΡΡΠ½ΠΎΡΡΠΈ
Fractal Image Compression Using Modified Operator (IFS)
Image data Compression based on fractal theory is fundamentally dierent from conventional compression methods, its idea is to generate a contraction operator whose fixed point approximates the original image in a complete metric space of images. The specication of such operator can be stored as the fractal code for the original image. The contraction mapping principle implies that the iteration of the stored operator starting from arbitrary initial image will recover its xed point which is an approximation for the original image. This Contraction mapping is usually constructed using the partitioned IFS(PIFS) technique which relies on the assertion that parts of the image resemble other parts of the same image. It then, nds the fractal code for each part by searching for another larger similar part. This high costly search makes fractal image compression dicult to be implemented in practice, even it has the advantages of a high compression ratio, a low loss ratio, and the resolution independence of the compression rate. In this paper, we investigate fractal image compression(FIC) using Iterated Function Systems(IFS). After reviewing the standard scheme, we state a mathematical formulation for the practical aspect. We then propose a modied IFS that relies on the fact that, there are very smooth parts in certain images. From the view point of mathematics, we present the modied operator, proving its properties that make it not only a fractal operator but also more eective than the standard one. The experimental results are presented and the performance of the proposed algorithm is discussed
Distributed video through telecommunication networks using fractal image compression techniques
The research presented in this thesis investigates the use of fractal compression techniques for a real time video distribution system. The motivation for this work was that the method has some useful properties which satisfy many requirements for video compression. In addition, as a novel technique, the fractal compression method has a great potential. In this thesis, we initially develop an understanding of the state of the art in image and video compression and describe the mathematical concepts and basic terminology of the fractal compression algorithm. Several schemes which aim to the improve of the algorithm, for still images are then examined. Amongst these, two novel contributions are described. The first is the partitioning of the image into sections which resulted insignificant reduction of the compression time. In the second, the use of the median metric as alternative to the RMS was considered but was not finally adopted, since the RMS proved to be a more efficient measure. The extension of the fractal compression algorithm from still images to image sequences is then examined and three different schemes to reduce the temporal redundancy of the video compression algorithm are described. The reduction in the execution time of the compression algorithm that can be obtained by the techniques described is significant although real time execution has not yet been achieved. Finally, the basic concepts of distributed programming and networks, as basic elements of a video distribution system, are presented and the hardware and software components of a fractal video distribution system are described. The implementation of the fractal compression algorithm on a TMS320C40 is also considered for speed benefits and it is found that a relatively large number of processors are needed for real time execution
Parallel implementation of fractal image compression
Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images
as a form of information redundancy that can be eliminated to achieve compression. This
theory based on Partitioned Iterated Function Systems is presented. As an alternative to the
established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal
techniques promise faster decoding and potentially higher fidelity, but the computationally
intensive compression process has prevented commercial acceptance.
This thesis presents an algorithm mapping the problem onto a parallel processor
architecture, with the goal of reducing the encoding time. The experimental work involved
implementation of this approach on the Texas Instruments TMS320C80 parallel processor
system. Results indicate that the fractal compression process is unusually well suited to
parallelism with speed gains approximately linearly related to the number of processors used.
Parallel processing issues such as coherency, management and interfacing are discussed. The
code designed incorporates pipelining and parallelism on all conceptual and practical levels
ensuring that all resources are fully utilised, achieving close to optimal efficiency.
The computational intensity was reduced by several means, including conventional
classification of image sub-blocks by content with comparisons across class boundaries
prohibited. A faster approach adopted was to perform estimate comparisons between blocks
based on pixel value variance, identifying candidates for more time-consuming, accurate
RMS inter-block comparisons. These techniques, combined with the parallelism, allow
compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB
PSNR. This is up to an order of magnitude faster than reported for conventional sequential
processor implementations. Fractal based compression of colour images and video sequences
is also considered.
The work confirms the potential of fractal compression techniques, and demonstrates
that a parallel implementation is appropriate for addressing the compression time problem.
The processor system used in these investigations is faster than currently available PC
platforms, but the relevance lies in the anticipation that future generations of affordable
processors will exceed its performance. The advantages of fractal image compression may
then be accessible to the average computer user, leading to commercial acceptance
Parallel implementation of fractal image compression
Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images
as a form of information redundancy that can be eliminated to achieve compression. This
theory based on Partitioned Iterated Function Systems is presented. As an alternative to the
established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal
techniques promise faster decoding and potentially higher fidelity, but the computationally
intensive compression process has prevented commercial acceptance.
This thesis presents an algorithm mapping the problem onto a parallel processor
architecture, with the goal of reducing the encoding time. The experimental work involved
implementation of this approach on the Texas Instruments TMS320C80 parallel processor
system. Results indicate that the fractal compression process is unusually well suited to
parallelism with speed gains approximately linearly related to the number of processors used.
Parallel processing issues such as coherency, management and interfacing are discussed. The
code designed incorporates pipelining and parallelism on all conceptual and practical levels
ensuring that all resources are fully utilised, achieving close to optimal efficiency.
The computational intensity was reduced by several means, including conventional
classification of image sub-blocks by content with comparisons across class boundaries
prohibited. A faster approach adopted was to perform estimate comparisons between blocks
based on pixel value variance, identifying candidates for more time-consuming, accurate
RMS inter-block comparisons. These techniques, combined with the parallelism, allow
compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB
PSNR. This is up to an order of magnitude faster than reported for conventional sequential
processor implementations. Fractal based compression of colour images and video sequences
is also considered.
The work confirms the potential of fractal compression techniques, and demonstrates
that a parallel implementation is appropriate for addressing the compression time problem.
The processor system used in these investigations is faster than currently available PC
platforms, but the relevance lies in the anticipation that future generations of affordable
processors will exceed its performance. The advantages of fractal image compression may
then be accessible to the average computer user, leading to commercial acceptance