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28th Irish signals and Systems Conference (ISSC)

作者： Blue, Juanita Condell, Joan Lunney, Tom Ulster Univ Intelligent Syst Res Ctr Derry North Ireland

ISBN: (纸本)9781538610466

The term 'steganography' encapsulates the practice of secretly embedding data into digital mediums including video, image and audio files. Although steganography is often associated with nefarious activities, conceptually it asserts several characteristics that render it useful in contemporary security applications. Not just a mechanism for criminals to communicate secret information over a digital channel, steganography is also used as a legitimate method of ensuring integrity of digital media artefacts and for identification of same. This application of steganography allows for identification images storing additional information to verify both the identity of the subject as well as the authenticity of the image. Developed methods of steganography invoke various spatial domain techniques that are successful in covertly concealing data within 'innocent' carrier images. The techniques include linear methods such as those which replace the least significant bit (LSB) of the bytes in an image and frequency domain methods including discrete cosine transform (DCT), discrete wavelet transform (DWT) and discrete Fourier transform (DFT). The success of a steganographic algorithm is hinged on the method's ability to successfully embed data, so that the data remains concealed within a carrier image;and also to successfully extract the same data uncorrupted. Often modern image coding formats include lossy compression in the frequency domain;this can result in data loss, corruption and noise within the image when carrier images are re-encoded. To ensure data extraction is successful, error correction functions must be invoked to counteract noise and ensure embedded data is extracted without any loss or corruption. In exploring steganographic software, the functionality and reliability of a novel steganographic application 'Intelligent Identity Authenticator' (IIA) was assessed. IIA invokes the use of steganography to conceal real-time identity information within images on iden

关键词： steganography image processing noise error-correction authentication

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An image compression method based on orthogonal wavelet packet transform

Boletin Tecnico/Technical Bulletin

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Boletin Tecnico/Technical Bulletin 2017年第1期55卷 18-25页

作者： Fan, Wenchao Wang, Xiaoling School of Computer Science and Engineering Cangzhou Normal University Cangzhou Hebei061001 China

The main reason why the image data can be compressed is that generally, the original image data is highly correlated and it contains plenty of redundant information. The purpose of image compression encoding is to erase the redundancy, represent and rebuild the image with as a small number of bits as possible with the given distortion in order to make it consistent with the requirements of the applications. By studying the nature and coefficient feature of each sub-band of the image after wavelet decomposition, this paper proposes an image compression method based on orthogonal wavelet packet transform. In other words, the mean image signal of the high-frequency components of the image is divided into four frequency bands after one wavelet transform, namely the high-frequency parts in the horizontal direction, the vertical direction and the diagonal direction and the low-frequency part, which will continue to decompose. In this way, the image signal has been decomposed to the sub-image signals different spatial resolutions, frequency characteristics and direction characteristics so that simultaneous processing can be achieved to long-term features of low frequency and short-term features of high frequency. In fact, orthogonal wavelet packet transform amounts to a low-pass filter. The main part an image presents itself is its low-frequency part while most of the high-frequency part is close to 0. The image compression based on orthogonal wavelet packet transform is to use its low-pass features to filter the highfrequency part and preserve the low-frequency part. The simulation experiment proves that the algorithm of this paper effectively overcomes the limitations existing in the complicated image compression in order to make the decomposition of the image signal more consistent with the visual characteristics of humans and the requirements of data compression.

关键词： Redundancy

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Application of multi-wavelet analysis in image compression

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Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 2016年第3期39卷 76-82页

作者： Zhang, Chunhong Langfang Teachers University Langfang Hebei065000 China

wavelet analysis has been widely applied in many fields such as image processing as well as signal representation and analysis with its unique characteristics of time-frequency localization. To use multi-wavelet in the image compression is an important aspect of the application of wavelet. However, most of the multi-scale function of the existing wavelet can meet the low-pass property and how to convert one-dimensional signal as the vector input flow deserves further research. Bi-orthogonal wavelet has compact support, high-order vanishing moments and symmetry and its construction theory has attracted extensive attention and research from the people. This paper explores the applications of multi-wavelet in the image compression from the perspective of bi-orthogonal multi-wavelet and proposes the idea to use bi-orthogonal balanced multi-wavelet algorithm in the image compression. The result of the simulation experiment shows that to use this method in the image compression can obtain a higher peak signal to noise ratio and a relatively ideal compression ratio.

关键词： image analysis

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Contrast Sensitivity of the wavelet, Dual Tree Complex wavelet, Curvelet, and Steerable Pyramid Transforms

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IEEE TRANSACTIONS ON image processing 2016年第6期25卷 2739-2751页

作者： Hill, Paul Achim, Alin Al-Mualla, Mohammed E. Bull, David Univ Bristol Dept Elect & Elect Engn Bristol BS8 1UB Avon England Khalifa Univ Sci Technol & Res Abu Dhabi 127788 U Arab Emirates

Accurate estimation of the contrast sensitivity of the human visual system is crucial for perceptually based image processing in applications such as compression, fusion and denoising. Conventional contrast sensitivity functions (CSFs) have been obtained using fixed-sized Gabor functions. However, the basis functions of multiresolution decompositions such as wavelets often resemble Gabor functions but are of variable size and shape. Therefore to use the conventional CSFs in such cases is not appropriate. We have therefore conducted a set of psychophysical tests in order to obtain the CSF for a range of multiresolution transforms: the discrete wavelet transform, the steerable pyramid, the dual-tree complex wavelet transform, and the curvelet transform. These measures were obtained using contrast variation of each transforms' basis functions in a 2AFC experiment combined with an adapted version of the QUEST psychometric function method. The results enable future image processing applications that exploit these transforms such as signal fusion, superresolution processing, denoising and motion estimation, to be perceptually optimized in a principled fashion. The results are compared with an existing vision model (HDR-VDP2) and are used to show quantitative improvements within a denoising application compared with using conventional CSF values.

关键词： Discrete wavelet transforms contrast sensitivity function

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Hybrid technique for robust image watermarking using discrete time fourier transform

Hybrid technique for robust image watermarking using discret...

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IEEE signal processing and Communications applications (SIU)

作者： Omar Abodena Mary Agoyi Erbug Celebi Department of Management Information Systems Cyprus International University Lefkosa Turkey Department of Computer Engineering Cyprus International University Lefkosa Turkey

ISBN: (纸本)9781509064953

The current paper proposes a novel scheme for non-blind watermarking of images, making use of discrete wavelet transform (DWT), discrete time Fourier transform (DTFT), as well as singular value decomposition, or SVD. During the process of embedding, 1-level DWT is used to decompose the host image into its various frequency sub-bands. After this, the high-frequency sub band receives an application of DTFT. This is followed then by SVD, after which the watermark becomes embedded into the now-transformed host image's singular matrix. Then, the inverses of 1-level DWT, DTFT and SVD are applied in order to obtain a watermarked final image. This paper evaluates the performance of the proposed method of watermarking against a number of attacks, including sharpening, salt and pepper noise, AWGN, gamma correction, histogram equalisation, flipping and cropping. Results obtained during experiments have found that the scheme as proposed does provide high levels of robustness and imperceptibility against various signal processing attacks.

关键词： Watermarking Discrete wavelet transforms Robustness Correlation coefficient Matrix decomposition Histograms

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A robust blind watermarking scheme based on stationary wavelet transform

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Journal of Information Hiding and Multimedia signal processing 2017年第3期8卷 676-687页

作者： Shokrollahi, Zahra Yazdi, Mehran Department of Electrical and Computer Engineering Shiraz University Zand St. Shiraz Iran

image watermarking is being used for proving the authenticity of images and videos. Concerning web applications, image watermarking is increasingly used to attest the ownership of images distributed on the web, however, it needs lightweight and robust watermarking methods for inserting and extracting watermarks. Recently, the stationary wavelet transform (SWT) which is linear, shift-invariant, redundant and undecimated shows a better performance in image watermarking. This paper presents a robust water- marking algorithm based on SWT which uses all subband decompositions of the image to embed the watermark and creates the key. In the extracting process, the key is used to extract the watermark from all subbands. The proposed algorithm has been compared with the more recent algorithm which uses SWT for image watermarking and the results using NCC and PSNR as evaluation metrics show a significant improvement in the presence of various attacks. © 2017.

关键词： image watermarking

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MAMMOGRAM COMPRESSION TECHNIQUES USING HAAR wavelet AND QUADTREE DECOMPOSITION-BASED image ENHANCEMENT

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BIOMEDICAL ENGINEERING-applications BASIS COMMUNICATIONS 2017年第5期29卷

作者： Mohamed, Basma A. Afify, Heba M. MTI Univ Engn Dept Bioelect Engn Cairo Egypt

Biomedical image compression plays an important role in the medical field. Mammograms are medical images used in the early detection of breast cancer. Mammogram image compression is a challenging task because these images contain information that occupies huge size for storage. The aim of image compression is to reduce the image size and the time taken for recovering the original image without any loss. In this paper, two different techniques of mammogram compression are introduced. The proposed algorithm includes two main steps. First, a preprocessing step is applied to enhance the image, and then a compression algorithm is applied to the enhanced image. The algorithm is tested using 322 mammogram images from the online MIAS database. Three parameters are used to evaluate the performance of the compression techniques;compression ratio (CR), Peak signal to Noise Ratio (PSNR) and processing time.& para;& para;According to the results, Haar wavelet-based compression for enhanced images is better in terms of CR of 26.25% and PSNR of 47.27dB.

关键词： Mammogram image compression MIAS database Compression ratio (CR) Peak signal to noise ratio (PSNR) Haar wavelet

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Determination of benign and malign lesions by fusion of the different phases of liver MR

Determination of benign and malign lesions by fusion of the ...

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IEEE signal processing and Communications applications (SIU)

作者： Saim Ervural Murat Ceylan Elektrik - Elektronik Mühendisliği Bölümü KTO Karatay Üniversitesi Konya Türkiye Elektrik - Elektronik Mühendisliği Bölümü Selçuk Üniversitesi Konya Türkiye

ISBN: (纸本)9781509064953

In this study, different phases of T1-weighted, dynamic contrast-enhanced liver magnetic resonance (MR) images were combined with wavelet-based image fusion to support decisions of radiologists. Used images has labelled as 6 different focal lesion types which focal nodular hyperplasia (FNH), hemangioma, cyst, colangiocellular carcinoma (CCC), hepatocellular carcinoma (HCC) and liver metastases. In application used images are taken by 4 different phases called pre-contrasted, arterial, portal venous, and delay venous from 30 patient. images registered with efficient subpixel registration by cross correlation method. Discrete wavelet transform(DWT) based image fusion algorithm used and maximum selection method applied as fusion rule. As result 180 fused images obtained The performances of fusion results compared with structural similarity index (SSIM), peak to noise ratio (PSNR) and fusion factor (FF) metrics. In the fusion of portal venous phase and delay venous phase images, 98.7% SSIM and 74.95 dB PSNR values were obtained, respectively. FF value in the fusion of pre-contrast phase & arterial phase images measured as 7.258. In comparison of lesion types were represented with 98.5% SSIM

关键词： Portals image fusion Liver wavelet transforms Lesions signal resolution

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Evolutionary design of multiplierless lifting-based 2D DWT filters for low-resolution image processing

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MULTIMEDIA TOOLS AND applications 2016年第16期75卷 9949-9972页

作者： Chen, Ching-Yi Hsia, Chin-Hsien Yang, Chun-Yuan Ming Chuan Univ Dept Informat & Telecommun Engn Taoyuan Taiwan Chinese Culture Univ Dept Elect Engn Taipei Taiwan

The most difficult challenge in developing a high-efficiency embedded image processing system relates to how to execute complicated Digital signal processing (DSP) algorithm with embedded processor, especially when processing high-resolution images. A whole system may start to run very slowly because of the large volumes of data being processed and the complexity of the algorithm, rendering it unable to achieve real-time processing. Executing motion detection in low-resolution image was already proved to be able to reduce the loading of data manipulation and to be able to help filter out noise and fake motion. However, smoothing filter pointed out in the work will affect the quality of image. Although the LL-band signal of a 2D Discrete wavelet Transform (DWT) domain can retain the main part of the energy and information of the original image, a complex algorithm is more suitable for the application of a low-pass filter. Symmetric Mask-based DWT (SMDWT) has the advantage of reduced complexity, regular signal coding, and independent subband coding processing. In this paper, we use a Particle Swarm Optimization (PSO) approach to automatically evolve the multiplierless 2D SMDWT filters hardware architecture. The architecture employs only shift-and-addition operations to replace the complex floating-point multiplication and division operations. It can use shift-and-add based SMDWT filter to decompose a low-resolution image and do real-time image processing system design with low-resolution image processing technique.

关键词： Real-time processing Discrete wavelet Transform Low-resolution Particle Swarm Optimization

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Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional wavelet Transform

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2016年第2期7卷 22-35页

作者： Zhao, Di Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352x288, 480x320, 720x480 and 1280x720, while cDWT can only obtain read-time processing with small image sizes of 352x288 and 480x320.

关键词： Coefficient Based Parallelization for DWT Mobile GPU Computing signal Based Parallelization for DWT SoC GPU Three-Dimensional Discrete wavelet Transform Video processing

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