Transform Domain Methods Research Articles

An enhanced direct chord transformation for music retrieval in the AAC transform domain with window switching

With the explosive growth in the number of music albums produced, retrieving music information has become a critical aspect of managing music data. Extracting frequency parameters directly from the compressed files to represent music greatly benefits processing speed when working on a large database. In this study, we focused on advanced audio coding (AAC) files and analyzed the disparity in frequency expression between discrete Fourier transform and discrete cosine transform, considered the frequency resolution to select the appropriate frequency range, and developed a direct chroma feature-transformation method in the AAC transform domain. An added challenge to using AAC files directly is long/short window switching, ignoring which may result in inaccurate frequency mapping and inefficient information retrieval. For a short window in particular, we propose a peak-competition method to enhance the pitch information that does not include ambiguous frequency components when combining eight subframes. Moreover, for chroma feature segmentation, we propose a simple dynamic-segmentation method to replace the complex computation of beat tracking. Our experimental results show that the proposed method increased the accuracy rate by approximately 7 % in Top-1 search results over transform-domain methods described previously and performed nearly as effectively as state-of-the-art waveform-domain approaches did.

A fast color image enhancement algorithm based on Max Intensity Channel

In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details.

Quality Assessment of Image Fusion Methods in Transform Domain

Quality Assessment of Image Fusion Methods in Transform Domain

Research On Underwater Target Signal Detection And Recognition Processing Algorithm

Abstract Practical application of underwater target echo signal usually get disturbed a Gaussian noise and non-Gaussian noise, in view of the signal recognition problem, this paper proposes a double spectrum analysis based on wavelet transform domain method of weak signal and D-S data fusion algorithm. Through the study of double spectrum of the wavelet transform domain analysis to the signal processing algorithm, the characteristics of the target signal and noise source signal, a processing method of underwater target signal de-noising was presented. For multiple underwater target recognition, the model of underwater target multi-sensor signal recognition was studied. On the basis of analyzing the principle of D-S method and the fusion of multiple signal recognition, the concrete measures of D-S data fusion reasoning was researched and analyzed. By using the combination of simulation calculation and experiment measures, the results show the signal processing method is correct.

Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval

In this paper, a new image indexing and retrieval algorithm using local mesh patterns are proposed for biomedical image retrieval application. The standard local binary pattern encodes the relationship between the referenced pixel and its surrounding neighbors, whereas the proposed method encodes the relationship among the surrounding neighbors for a given referenced pixel in an image. The possible relationships among the surrounding neighbors are depending on the number of neighbors, P. In addition, the effectiveness of our algorithm is confirmed by combining it with the Gabor transform. To prove the effectiveness of our algorithm, three experiments have been carried out on three different biomedical image databases. Out of which two are meant for computer tomography (CT) and one for magnetic resonance (MR) image retrieval. It is further mentioned that the database considered for three experiments are OASIS-MRI database, NEMA-CT database, and VIA/I-ELCAP database which includes region of interest CT images. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, LBP with Gabor transform, and other spatial and transform domain methods.

Radiometric Correction of Multispectral Images Using Radon Transform

Satellite imagery analysis has played a key role in environmental monitoring and modeling over the past few decades. Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the gray value differences caused by non-surface factors such as different illumination, atmospheric or sensor conditions. Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. This paper presents methodology for correction of Landsat TM images in Radon domain. We propose radiometric correction using Radon Transform based regression method. The transform domain method statistically determines correction values based on contrast between spectral properties of various homogeneous areas. TM band 1 is the band most affected by atmospheric scattering whereas TM band 5 is less affected by atmospheric scattering effect. Hence TM Band 5 is taken as the reference image. Results are assessed statistically and compared with results of popular spatial domain Regression Line Method. The application of the methods for vegetation analyses is also shown in this paper. Test results show that the method gives improved results in removal of atmospheric influence.

Spatial and Transform Domain Filtering Method for Image De-noising: A Review

Present investigation reveals the quantum of work carried in the filtering methods for image denoising. An image is often gets corrupted by various noises that are visible or invisible while being gathered, coded, acquired and transmitted. Noise influences various process parameters that may cause a quality problem for further image processing. De-noising of natural images is appears to be very simple however when considered under practical situations becomes complex. It has been cited by various author that parameter such as type and quantum of noise, image etc. through single algorithm or approach becomes cumbersome when results are optimized. In order to improve the quality of an image noise must be removed when the image is pre-processed and the important signal features like edge details should be retained as much as possible. The search on efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. This paper reviews significant de-noising methods (spatial and transform domain method) and their salient features and applications. One filter in each category has been taken in consideration to understand the characteristics of both spatial and transform domain filters.

A Modified Adaptive PCA Learning based Method for Image Denoising

The paper deals with image denoising with a new approach towards obtaining high quality denoised image patches using only a single image. A learning technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. this paper show a framework for denoising by learning an appropriate basis function to describe image patches after applying transform domain method on noisy image patches. Such basis functions are used to describe geometric structure. The algorithm maps have been applies on LR patch space to generate the HR one, generating HR patch. Using this strategy, more patch patterns can be represented using a smaller training database. In super resolution (SR), the goal is not sparse representation, but sparse recovery. Furthermore try to make some modify on local window before perform PCA transform on it this modify include, change number of iteration according to the amount of noise on image additionally using the benefited of steering kernel regression (SKR) to prepare the noisy image before apply LPG-PCA. While kernel regression (KR) is a well studied method in statistics and signal processing, KR is identified as a nonparametric approach that requires minimal assumptions, and hence the framework is one of the appropriate approaches to the regression problem.

Remote Sensing Image Fusion Based on Discrete Fractional Random Transform for Modified IHS

Abstract. The traditional IHS fusion, as a spatial domain method, mostly achieves the good spatial quality but the spectral distortion. The discrete fractional random transform (DFRNT) fusion, as a transform domain method, can maintain the high spectral information. Thus, in this paper, we propose a combined DFRNT-IHS approach to obtain good spectral quality and high spatial quality. Moreover, instead of fusion in spatial domain or transform domain respectively as in the conventional way, the proposed DFRNT-IHS approach is for joint spatial and transform domain. Real multi-spectral and panchromatic images are used to evaluate the effectiveness of the proposed method. The fused images are found to preserve both the spectral information of the multi-spectral image and the high spatial resolution information of the panchromatic image more effectively than several existing image fusion techniques. Spectrum distribution of DFRNT is random and dispersive, which guarantees low spectral distortion and certain robustness.

Sign LTP and magnitude LTP for image indexing and retrieval

In this paper, a new algorithm for image indexing and retrieval is proposed by using the combinations of sign and magnitude of local ternary patterns (LTPs), which are calculated from the Local Difference Operator (LDO). The LDO separates the local region of the image into two components (the sign and the magnitude). The sign LTP operator (SLTP) is a generalised LTP operator and the magnitude LTP operator (MLTP) is calculated using the magnitude LDO (MLDO), weighted by the mean of the MLDO. The retrieval results of the proposed method are tested on three different image databases, i.e., the Corel 1000 (DB1), the Brodatz database (DB2) and the MIT VisTex database (DB3). The results after investigation show a significant improvement in terms of average retrieval precision and average retrieval rate as compared to previously reported spatial and transform domain methods.

A Block Based Novel Digital Video Watermarking Scheme Using Dct

The rapid growth of network distributions of images and video, there is a need for copyright protection against piracy. For this purpose we are using video watermarking (VM). Different digital watermarking schemes have been proposed to address this issue of ownership identification. Digital watermarking is a process by which user specified signal(Watermark) is hidden or embedded into another signal(Video Signal) by the watermark embedding process. Afterwards the recovery of the watermark is achieved with the help of the watermark extraction process. The video watermark is robust against the attack of frame dropping, averaging and statistical analysis. It leads to broad curiosity in multimedia security and multimedia copyright protection. The most important issue in VM are the invisibility of the watermark and the resilience of watermarking to attacks.Watermarking techniques are classified into three categories. They are Spatial Domain Method(SDM), Transform Domain Method(TDM) and Compressed Domain Method(CDM). Here explained about to transform domain method. This method used the discrete cosine transform(DCT), Discrete Fourier Transform(DFT) and Discrete Wavelet Transform(DWT) for watermark embedding.DWT are more popularity due to their spatial localization, frequency spread etc.. But DCT watermarking was to human perception model to modify low coefficients of DCT blocks.It has a larger embedding capacity and robustness. This technique provides better results with high accuracy.

Texture Based Image Retrieval Using Framelet Transform–Gray Level Co-occurrence Matrix(GLCM)

This paper presents a novel content based image retrieval (CBIR) system based on Framelet Transform combined with gray level co-occurrence matrix (GLCM).The proposed method is shift invariant which captured edge information more accurately than conventional transform domain methods as well as able to handle images of arbitrary size. Current system uses texture as a visual content for feature extraction. First Texture features are obtained by computing the energy, standard deviation and mean on each sub band of the Framelet transform decomposed image .Then a new method as a combination of the Framelet transform-Gray level co-occurrence matrix (GLCM) is applied. The results of the proposed methods are compared with conventional methods. We have done the comparison of results of these two methods for image retrieval. Euclidean distance, Canberra distance, city black distance is used as similarity measure in the proposed CBIR system.

How to SAIF-ly Boost Denoising Performance

Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive regression kernel) have achieved great success in denoising. Their overall performance, however, has not generally surpassed the leading transform domain-based filters (such as BM3-D). One important reason is that spatial domain filters lack efficiency to adaptively fine tune their denoising strength; something that is relatively easy to do in transform domain method with shrinkage operators. In the pixel domain, the smoothing strength is usually controlled globally by, for example, tuning a regularization parameter. In this paper, we propose spatially adaptive iterative filtering (SAIF) is the Middle Eastern/Arabic name for sword. This acronym somehow seems appropriate for what the algorithm does by precisely tuning the value of the iteration number. a new strategy to control the denoising strength locally for any spatial domain method. This approach is capable of filtering local image content iteratively using the given base filter, and the type of iteration and the iteration number are automatically optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the estimated local signal-to-noise-ratio, we also present a new risk estimator that is different from the often-employed SURE method, and exceeds its performance in many cases. Experiments illustrate that our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing) parameters, and effectively boost the performance of several existing denoising filters to generate state-of-the-art results under both simulated and practical conditions.

A Comparative Analysis of Steganographic Data Hiding within Digital Images

A huge deployment of effective steganography by several techniques with varying degrees of payload, peak signal to noise ratio (PSNR), robustness, perceptual transparency and so on has been evidenced. Steganography has been effective as an alternative to cryptography and has been projected to the forefront of digital security by the explosive growth in computational power, security awareness and through widespread intellectual pursuit. The current techniques for Steganography reviewed include substitution method such as least significant bit (LSB) and transform domain methods such as discrete cosine transform (DCT) and discrete wavelet transform (DWT). In this paper, the techniques involved with LSB, DCT and DWT are analyzed with the proper use of the performance metrics and then the techniques have been modeled by computer simulations. The results from simulation indicate that the LSB technique, although easy to implement and encode and having good payload capacity, is easily prone to statistical attack, since the histogram plots are revealing the data hidden within. On the other hand, transform domain techniques are more robust to statistical attacks but at the cost of reduced payload.

Transform domain methods for performance enhancement of EFPI sensor

The paper presents advances in the processing technique based on transform domain methods for performance enhancement of extrinsic Fabry–Perot interferometric (EFPI) sensors. The paper introduces a new technique to estimate the cavity length from the instantaneous phase. Pre-processing of the EFPI spectrum signal is proposed using wavelet transform to filter out the low frequency background and broadband measurement noise. The filtering technique efficiently extracts the required interference spectrum in a low cavity length EFPI. The computation of the analytic representation of the spectrum signal has been carried out using Hilbert transform. The variation computed using the proposed algorithm in a continuous test with an EFPI of cavity length 66μm is found to be as low as ±1.13nm. The range of wavelength used for processing the EFPI signal for accurate estimation of cavity length also needs to be optimized. In this regard, a definite relation between the wavelength range and cavity length measurement stability have been found. Optimization improves sensor response time and multiplexing ability.

A Survey of Transform Domain based Semifragile Watermarking Schemes for Image Authentication

Image authentication is key research area for researchers’ world over due to its important function in multimedia content authentication. Hence it’s essential to check integrity of images before it can be put to use, in some applications such as medical, military and quality control images. In order to protect authenticity of images many approaches have been proposed, semifragile watermarking scheme is found useful in most of the practical applications. The aim of this paper is to present survey and comparison of transform domain based semifragile watermarking techniques for image authentication. Different transform domain methods used for semifragile watermark embedding are compared based on peak signal to noise ratio and mean square error. Comparative study reflects that selection of methods and parameters are application specific. Furthermore important requirements for an effective image authentication algorithm are also discussed.

Human and Nonhuman Recognition Using Pyroelectric Infrared Detector

The auto regressive (AR) model of time series is utilized in this paper to recognize a human and nonhuman from pyroelectric infrared (PIR) signals. Through the wavelet transform, the signals are reconstructed by removing the noise from the original signals. The coefficients of the AR model are selected as the features for human and nonhuman recognition and calculated by the Burg algorithm. The classification experiments of a human and nonhuman are performed with a support vector machine. The recognition results for different PIR signals using the proposed AR-based features show high performance with an optimal recognition rate, which is up to 94.6 % and higher than that of the traditional time domain feature and transform domain method, such as the wavelet entropy and wavelet entropy of the double-density dual-tree complex wavelet transform.

Directional local extrema patterns: a new descriptor for content based image retrieval

In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner that it extracts the directional edge information based on local extrema in 0 $$^{\circ }$$ , 45 $$^{\circ }$$ , 90 $$^{\circ }$$ , and 135 $$^{\circ }$$ directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.

Line edge binary patterns for content-based image retrieval

In this paper, a new algorithm known as Line Edge Binary Pattern (LEBP) is proposed for Content-Based Image Retrieval (CBIR). The proposed method represents the local region of an image based on the distribution of line edges in contrast to Local Binary Patterns (LBPs). LBPs extract information based on the distribution of point edges which are evaluated by taking into consideration local differences between the centre pixel and its neighbours in an image. The retrieval results of the proposed method are tested on Corel 1000 and Corel 2450 image databases in terms of precision and recall. The results after being investigated show a significant improvement in terms of precision and recall as compared to LBP and other existing spatial and transform domain methods.

Efficient transform domain transcoding: intra frame of H.264/AVC to JPEG

In this paper, we propose an efficient transform domain method for transcoding an intra frame of H.264/AVC to a JPEG image. First, we show how to obtain the transform domain predictions for various intra modes of H.264/AVC through simplified matrix multiplication. Then, we describe a transform domain operation corresponding to the rounding operation which is performed after computing the weighted average during spatial domain intra prediction. Finally, we convert 4 × 4 integer transform blocks to 8 × 8 DCT blocks by defining a transform kernel matrix based on the factorization of transform matrices. Experimental results demonstrate that the proposed algorithm can reduce the total computational complexity about 16% on average as compared with the existing methods, and outperforms them in PSNR by 1.5~2.5dB.