An outdoor time scenes simulation scheme based on support vector regression with radial basis function on DCT domain

디지털 워터마킹은 저작권 보호와 멀티미디어 데이터 인증의 수단으로 사용되어 왔다. 기존의 공간영역에서의 CRT 기반 워터마킹 방법은 JPEG 압축에 취약하여, JPEG 압축에서 잘 동작하는 DCT 영역에서의 CRT 기반 워터마킹이 제안되었다. 본 논문에서는 DCT 영역에서의 향상된 CRT 기반 워터마킹 방법을 제안한다. 제안한 방법은 DCT 변환시 발생되는 rounding 오류 또는 여러 가지 공격에도 나머지들의 차에 대한 절대값의 변화를 감소시켜 견고성을 향상시킨다. 실험을 통하여 제안한 방법이 기존의 DCT 영역에서의 CRT 기반 워터마킹 방법보다 여러 가지 공격에 대하여 견고성이 향상되는 것을 확인하였다. Digital watermarking techniques have been used as one of the means for copyright protection and authentication of multimedia data. Conventional Chinese Remainder Theorem(CRT)-based spatial domain watermarking techniques do not perform well under JPEG compression. However, it is seen that the CRT-based watermarking technique in Discrete Cosine Transform(DCT) domain performs well for JPEG compression. In this paper, an improved CRT-based image watermarking method in the DCT domain is proposed. The proposed method provides better robustness which decreases changes of absolute difference of residues against rounding errors due to DCT conversion and various attacks. Experimental results show that the proposed method has a good robustness against various attacks compared with the conventional CRT-based watermarking in DCT domain.

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location.

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works.

Review of <i>Environmental and Hydrological Systems Modeling</i> by A. W. JayawardenaCRC Press Taylor &amp; Francis Group, Boca Raton, FL 33487; 2014; ISBN 978-0-415-46532-8; 516 pp.; $99.95.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

A new global image restoration scheme using discrete cosine transform (DCT) is proposed in this paper. This DCT-based image restoration scheme can be used for inspecting the defects in directional texture surfaces automatically. The input spatial domain image is first transformed into DCT domain. The dominating directions of the textures in the input image will be compacted to orthogonal straight lines, respectively, throughout the direct current (DC) component in the spectrum. The linear primitives associated with high-energy frequency components in the DCT domain are eliminated by reducing them to zero and then transformed back to the spatial domain. This procedure will have all directional textures be blurred and will preserve only local defects if they are initially embedded in the input image. Experiments on a variety of product surfaces with directional texture such as straight, slant, orthogonal, and slant orthogonal linear primitives are given to demonstrate the effectiveness and robustness of the proposed method.

Outdoor scene analysis is a complex problem for both image processing and pattern recognition domains. This paper proposes an approach for labeling regions in outdoor scene images. The basic idea of this approach is to label local image regions into semantic objects such as tree, sky and road etc. There are four phases in the approach: segmentation, feature extraction, region labeling and merging. Firstly, modified Marker-Controlled Watershed (MCWS) algorithm proposes for segmented regions generation. And then, color feature extracted from segmented regions are given as input to 3-layer Artificial Neural Network (ANN) classifier for labeling. Finally, region merging is performed if the two regions are adjacent with the same color values. The proposed method is test on our real scene image dataset which are collected from our university campus.

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

Steganography is the art and science of hiding data in different media such as texts, audios, images, and videos. Data hiding techniques are generally divided into two groups: spatial and frequency domain techniques. Spatial domain methods generally have low security and, as a result, are less attractive to researchers. Discrete cosine transform (DCT) is the most common transform domain used in steganography and JPEG compression. Since a large number of the DCT coefficients of JPEG images are zero, the capacity of DCT domain-based steganography methods is not very high. We present a high-capacity method for hiding messages in the DCT domain. We describe the method in two classes where the receiver has and where the receiver does not have the cover image. In each class, we consider three cases for each coefficient. By considering n coefficients, there are 3 n different situations. The method embeds ⌊log 2 3 n ⌋ bits in these n coefficients. We show that the maximum reachable capacity by our method is 58% higher than the other general steganography methods. Experimental results show that the histogram-based steganalysis methods cannot detect the stego images produced by the proposed method while the capacity is increased significantly.

Many effective watermarking algorithms have been proposed and implemented for digital images and digital video. However, a few algorithms have been proposed for audio watermarking. This is due to the fact that, human auditory system (HAS) is far more complex and sensitive than human visual system (HVS). In this research work, a new method of embedding image data into the audio signal and additive audio watermarking algorithm based on Discrete Cosine Transformation (DCT) domain is proposed. First, the original audio is transformed into DCT domain. The DCT coefficients are divided into a fixed number of subsections and the energy of each subsection is calculated. Next, watermark is generated from image by image processing algorithm. Watermarks are then embedded into selected peaks of highest energy subsection. Experimental results demonstrate that the watermark is inaudible and this algorithm is robust to common operations of digital audio signal processing, such as noise addition, re-sampling, requantization and so on. To evaluate the performance of the proposed audio watermarking method, subjective and objective quality tests including Bit Error Rate (BER) and Signal to Noise ratio (SNR) are conducted.

Light intensity, CO2 concentration, and temperature are three primary environmental factors with high temporal variability, nonlinearity, and strong coupling, which directly influence the photosynthetic rate of plants. To investigate the combined influence of these factors on the photosynthetic rate of chili pepper plants, a predictive model was developed for their net photosynthetic rate (Pn) during the peak fruiting period. A multifactorial nested experimental design with irregular sampling intervals was used to systematically examine the interacting effects of light intensity, CO2 concentration, and temperature on photosynthesis. Based on the collected data, a support vector regression (SVR) algorithm was trained and its performance was compared with that of a backpropagation (BP) neural network, a radial basis function (RBF) neural network, and a random forest (RF) algorithm. To optimize performance, a grid search with five-fold cross-validation was conducted to identify optimal hyperparameters; this process yielded a cost parameter (C) of 38 and a gamma parameter (γ) of 8, which minimized the root mean square error (RMSE) on the training set. On the test set, the SVR model achieved a coefficient of determination (R2) of 0.9941 and an RMSE of 0.6988 μmol m−2 s−1 (relative to the full Pn range of −4.19 to 39.2 μmol m−2 s−1). A linear fit between measured and predicted Pn values yielded a slope of 0.992 and an intercept of 0.07, indicating near-perfect agreement and surpassing the performance of the BP, RBF, and RF models. These results demonstrate that the SVR-based model outperformed the other approaches and exhibited superior predictive ability, establishing it as a robust theoretical foundation and a practical tool for dynamic environmental optimization in controlled-environment agriculture.

Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding. Inspired by this, we analyze the effect of content complexity on visual masking, and calculate the content complexity with the distribution of orientation. Then, by considering the effect from content complexity, contrast sensitivity function, and luminance adaption, a novel JND model in Discrete Cosine Transform (DCT) domain is proposed. Experimental results demonstrate the effectiveness of the proposed model for JND estimation.

To meet with the different client end devices and network bandwidth transmission requirements, it is necessary to convert the high definition resolution images and videos to standard definition resolution format. A novel approach to convert image resolution with arbitrary ratios in the DCT (Discrete Cosine Transform) domain is proposed, which exploits the relationship of a block and its subblocks with differing size. It can realize arbitrary unequal ratios in the horizontal and vertical direction, respectively. Unlike the present methods, it does not need upsampling- downsampling process. It can perform downsizing directly to the original data and confirms to the standard decoder. The proposed approach is computationally fast and memory efficient and produces visually better images with higher PSNR compared to the spatial methods.

The aim of image fusion is to combine similar information from multiple images into a single image. The methods which are based on discrete cosine transform (DCT) of image fusion are more competent and time-saving in real-time systems using DCT based standards of still Image. The existing DCT based methods are suffering from some side effects like blurring which can reduce the quality of the output image. To address this issue, the paper proposing new method for image fusion using Gaussian pyramid in DCT domain. The pyramid fusion provides better fusion quality. The execution time is extremely reduced, compare with existing methods. This method can be used for multi model image fusion as well as fusion of complementary images. The algorithm given in proposed system is simple and easy to implement. Also, it could be used for real time applications. The performance of our method is analyzed and compared with other image fusion methods. Experimental results show that there is no difference between the result of our method and water-based image fusion result. But our algorithm is carried out in DCT domain; it is efficient in processing time and simple.

The paper proposes an efficient transient audio coding algorithm based on Discrete Cosine Transform (DCT) and sine extraction with Matching Pursuit (MP), which can be used conveniently in the sinusoidal parametric audio coder. The overlapped input frame is firstly classified into transient and non-transient signal. The transient detection is designed based on the energy ratio of the overlapped subframe to the whole frame. The transient signal can be represented with sinusoidal model in the DCT domain. After DCT processing, the sinusoids are extracted based on MP algorithm. The transient coding parameters contain a series of amplitudes, frequencies and phases in the DCT domain. Finally, a 15.5kbps wide-band transient audio coder is designed and the synthesized transient signal performs a little better than the one coded by MPEG-4 sinusoidal parametric coder.