Wavelet Feature Family for Image Compression

Patlayenko, Mikola; Osharovska, Olena; Pyliavskyi, Volodymyr; Solodka, Valentina

doi:10.1109/telecom48729.2019.8994877

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…As a quantitative criterion is often used to estimate the probability of signal distortion during threshold filtering or filtering error [9], to reduce which it is advisable to use methods that use minimum threshold values that lead to less significant signal distortion. For this purpose, an alternative to the threshold task is widely used -the SURE method [10]. According to this method, as an estimate of the threshold level is considered the value of ( 5)…”

Section: аPplication Of the Threshold Functionmentioning

confidence: 99%

Filtracja Obrazów Szumowych W Oparciu O Przekształcenia Falkowe

SOLODKA¹,

OSHAROVSKAYA²,

PATLAIENKO³

2021

Przetwarzanie, Transmisja I Bezpieczeństwo Informacji

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Artykuł dotyczy filtrowania obrazów z różnych przeszkód metodami "twardego" i "miękkiego" zadania funkcji progowej przy filtrowaniu z wykorzystaniem transformacji falkowej. Wprowadzono kryteria ilościowe w celu porównania jakości filtrowania falkowego wykonanego przy użyciu różnych podejść. Zastosowanie filtru falkowego opartego na jednowymiarowym DWT z zakłóceniami jest zlokalizowane i konieczne jest wyeliminowanie go bez zniekształceń sygnału w tych obszarach, w których zakłócenia nie występują. Dlatego do ich korekty można zastosować kryterium amplitudy. Wybierając część współczynników falkowych, które mogą być skorygowane, można kontrolować jakość sygnału informacyjnego.Słowa kluczowe: transformata falkowa, kompresja obrazu, cyfrowe przetwarzanie sygnału, strumień cyfrowy. FILTRATION OF NOISE IMAGES BASED ON WAVELET TRANSFORMATIONS AbstactThe article considers the filtering of images from various obstacles, by the methods of "hard" and "soft" task of the threshold function when filtering using wavelet transformation. Quantitative criteria have been introduced to compare the quality of wavelet filtering performed using different approaches. The application of a wavelet filter based on one-dimensional DWT with interference is localized, and it is necessary to eliminate it without signal distortion in those areas in time where interference is absent. Therefore, the amplitude criterion can be used for their correction. By selecting the part of the wavelet coefficients that can be corrected, you can control the quality of the information signal.

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Section: аPplication Of the Threshold Functionmentioning

confidence: 99%

Filtracja Obrazów Szumowych W Oparciu O Przekształcenia Falkowe

SOLODKA¹,

OSHAROVSKAYA²,

PATLAIENKO³

2021

Przetwarzanie, Transmisja I Bezpieczeństwo Informacji

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“…Що стосується аналізу зображень процедура розкладання по вейвлет передбачає перехід до двовимірної реалізації дискретного вейвлетперетворення двовимірного ДВП [7]. Такий підхід, зокрема, використовується в комп'ютерній графіці в рамках формату JPEG2000.…”

Section: рис 3 схематичне уявлення одновимірного двп в рамках багато масштабного аналізуunclassified

“…Що стосується аналізу зображень формули (9) і (10) коригуються таким чином:  ).При проведенні дискретного вейвлет-перетворення число коефіцієнтів змінюється в 2 рази при переході від одного рівня дозволу до іншого. З цієї причини можуть застосовуватися як підходи на основі глобального введення порогу (С є фіксованою величиною, що не залежить від рівня дозволу) [7], так і більш гнучкі підходи, що передбачають завдання різних порогових рівнів Сj в залежності від роздільної здатності j.…”

Section: рис 3 схематичне уявлення одновимірного двп в рамках багато масштабного аналізуunclassified

Formation of Wavelet - Transform Signals Taking Into Account Threshold Functions in the Digital Stream Compression Problem

Solodka

Osharovska

et al. 2020

Visnyk Universytetu “Ukraina”

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In order to reduce the digital stream, the problem of improving the quality of digital filtering based on Discrete Wavelet Transformation (DWT) was analyzed using different options for setting the threshold function and the choice of wavelet basis. Given the considerable interest in this problem and the numerous studies concerning the search for ways to optimize the suppression of interference present in signals and images, the topic continues to be relevant and important. For such tasks as image compression, operations in processing and synthesis of different signals, in the analysis of images of different nature, in reducing (compressing) large amounts of information, and to protect information is often used wavelet transform, implemented on the basis of mirror filters. For the formation of wavelet-transform signals taking into account the threshold functions in the problem of digital stream compression, a standard approach to solving the problem of signal purification from interference and random distortions using Daubechies wavelet and adjusting the signal decomposition coefficients based on wavelet functions using soft and rigid threshold task options. It is shown that the use of complex bases provides an advantage both in terms of threshold filtering error and in terms of reducing the risk of accidental distortion in the reconstruction of the useful signal by wavelet coefficients. Appropriate steps were taken for the test signal and experimental data. When using the threshold function, the large modulus (most significant) wavelet coefficients remain unchanged, and the small ones are reset to zero. The change in the amplitude of the recovered signal leads in the latter case to a decrease in the absolute values of all wavelet coefficients, including large modulo. For those applications where it is important to keep the amplitude characteristics constant, this approach is not suitable, but there are problems where it is more important to maintain the regularity of the signal than to accurately reproduce its amplitude. Rhis is the filtering of images from various obstacles, where the method of "soft" task of the threshold function is a widely used approach. When analyzing signals, the constant amplitude is also not always a mandatory requirement. An audio signal can be amplified after filtering, and pre-cleaning it from interference is more important than changing the amplitude characteristics. The use of complex wavelet transform allowed to obtain error reduction and lower threshold level when adjusting wavelet coefficients, and can be recommended for analysis of complex wavelet transform methods as an effective tool for cleaning signals and images of different nature for further research.

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“…Wavelet is defined as a small wave that can be the base of all physical phenomena; which means that a time and/or space variation of a phenomenon is a sum of multiple wavelets. As examples, the wavelet transform was applied on an electrocardiogram (ECG) signal in order to extract the QRS complex [1] (time variation), on a video sequence in order to implement a hidden watermark [2] (time and space variation) and on a 2D image in order to reduce its size (compression) [3,4] (space variation). In this chapter, one considers the application of the wavelet on 2D image compression.…”

Section: Introductionmentioning

confidence: 99%

The Discrete Quincunx Wavelet Packet Transform

Bassou¹

2021

Wavelet Theory

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This chapter aims to present an efficient compression algorithm based on quincunx wavelet packet transform that can be applied on any image of size 128 × 128 or bigger. Therefore, a division process into sub-images of size 128 × 128 was applied on three gray-scale image databases, then pass each sub-image through the wavelet transform and a bit-level encoder, to finally compress the sub-image with respect to a fixed bit rate. The quality of the reconstructed image is evaluated using several parameters at a given bit rate. In order to improve the quality in sense of the evaluation quality, an exhaustive search has led to the best packet decomposition base. Two versions of the proposed compression scheme were performed; the optimal version is able to decrease the effect of block boundary artifacts (caused by the image division process) by 27.70 % considering a natural image. This optimal version of the compression scheme was compared with JPEG standard using the quality evaluation parameters and visual observation. As a result, the proposed compression scheme presents a competitive performance to JPEG standard; where the proposed scheme performs a peak signal to noise ratio of 0.88 dB over JPEG standard at a bit rate of 0.50 bpp for a satellite image.

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Wavelet Feature Family for Image Compression

Cited by 5 publications

References 2 publications

Filtracja Obrazów Szumowych W Oparciu O Przekształcenia Falkowe

Filtracja Obrazów Szumowych W Oparciu O Przekształcenia Falkowe

Formation of Wavelet - Transform Signals Taking Into Account Threshold Functions in the Digital Stream Compression Problem

The Discrete Quincunx Wavelet Packet Transform

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