Discrete Cosine Transform Compression Research Articles

Wide kernels and their DCT compression in convolutional networks for nuclei segmentation

The locality and spatial field of view of image operators have played a major role in image analysis, from hand-crafted to deep learning methods. In Convolutional Neural Networks (CNNs), the field of view is traditionally set to very small values (e.g. 3 × 3 pixels) for individual kernels and grown throughout the network by cascading layers. Automatically learning or adapting the best spatial support of the kernels can be done by using large kernels. Due to the computation requirements of standard CNN architectures, this has been little investigated in the literature. However, if large receptive fields are needed to capture wider contextual information on a given task, it could be learned from the data. Obtaining an optimal receptive field with few layers is very relevant in applications with a limited amount of annotated training data, e.g. in medical imaging.We show that CNNs (2D U-Nets) with large kernels outperform similar models with standard small kernels on the task of nuclei segmentation in histopathology images. We observe that the large kernels mostly capture low-frequency information, which motivates the need for large kernels and their efficient compression via the Discrete Cosine Transform (DCT). Following this idea, we develop a U-Net model with wide and compressed DCT kernels that leads to similar performance and trends to the standard U-Net, with reduced complexity. Visualizations of the kernels in the spatial and frequency domains, as well as the effective receptive fields, provide insights into the models’ behaviors and the learned features.

Design of Fuel Supply Curves for Aircraft Center of Gravity Balance Based on DCT Compression

The center of gravity (CG) has a significant influence on the controllability, stability, and fuel efficiency of the aircraft. For the aircraft with multiple fuel tanks, the CG position can be controlled by the fuel quality of different fuel tanks during flight. In this paper, an optimization method based on discrete cosine transform (DCT) compression is proposed to solve the optimization strategy of aircraft multi-tank fuel supply according to the current aircraft mission and engine working requirements. The transform coefficients of the low frequency components are used as design variables to represent the fuel supply curves. The improved KS function is proposed to deal with constraints. The numerical example is demonstrated to verify the effectiveness of the proposed method.

A hybrid compression method for the NMR data based on window averaging and Discrete Cosine Transform

A large amount of raw echo data is collected by NMR logging, especially in multi-dimensional NMR logging, and a direct inversion for the raw echo data is time-consuming, so the echo data collected need to be compressed before inversion to improve inversion speed. This paper proposes a multi-dimensional NMR data compression method based on Discrete Cosine Transform (DCT). Utilizing the energy concentration characteristics of DCT, the low-frequency information of the echo data is retained to realize the compression of raw echo data. On this basis, a hybrid compression (WAC-DCT) method is proposed, which uses the window averaging method (WAC) to pre-compress the raw echo data, and then uses the DCT compression method to compress the pre-compressed echo data again to achieve a high compression rate. The compression effects of the WAC method, the FastSVD method, the WAC-SVD method, the DCT method and the WAC-DCT method are compared by numerical simulation. The results show that some fluid information is lost in the DCT compression process with a low compression rate. The FastSVD method, the WAC-SVD method and the WAC-DCT method all have the characteristic of a high compression rate. When the number of compressed echoes is very small, the WAC-DCT method has obvious advantage in T1 distribution. Compared with the case of without compression, the WAC-DCT method not only ensures a high compression rate, but also effectively reduces the inversion time, which has a good application effect in multi-dimensional NMR data compression.

Efficient implementation of optical scanning holography in cancelable biometrics.

This paper presents a new trend in biometric security systems, which is cancelable multi-biometrics. In general, traditional biometric systems depend on a single biometric for identification. These traditional systems are subject to different types of attacks. In addition, a biometric signature may be lost in hacking scenarios; for example, in the case of intrusion, biometric signatures can be stolen forever. To reduce the risk of losing biometric signatures, the trend of cancelable biometrics has evolved by using either deformed or encrypted versions of biometrics for verification. In this paper, several biometric traits for the same person are treated to obtain a single cancelable template. First, optical scanning holography (OSH) is applied during the acquisition of each biometric. The resulting outputs are then compressed simultaneously to generate a unified template based on the energy compaction property of the discrete cosine transform (DCT). Hence, the OSH is used in the proposed approach as a tool to generate deformed versions of human biometrics in order to get the unified biometric template through DCT compression. With this approach, we guarantee the possibility of using multiple biometrics of the same user to increase security, as well as privacy of the new biometric template through utilization of the OSH. Simulation results prove the robustness of the proposed cancelable multi-biometric approach in noisy environments.

Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping.

Evolutionary information is crucial for the annotation of proteins in bioinformatics. The amount of retrieved homologs often correlates with the quality of predicted protein annotations related to structure or function. With a growing amount of sequences available, fast and reliable methods for homology detection are essential, as they have a direct impact on predicted protein annotations. We developed a discriminative, alignment-free algorithm for homology detection with quasi-linear complexity, enabling theoretically much faster homology searches. To reach this goal, we convert the protein sequence into numeric biophysical representations. These are shrunk to a fixed length using a novel vector quantization method which uses a Discrete Cosine Transform compression. We then compute, for each compressed representation, similarity scores between proteins with the Dynamic Time Warping algorithm and we feed them into a Random Forest. The WARP performances are comparable with state of the art methods. The method is available at http://ibsquare.be/warp. Supplementary data are available at Bioinformatics online.

A generalized DCT compression based density method for topology optimization of 2D and 3D continua

In this paper, a novel topology optimization method based on discrete cosine transform (DCT) and density interpolation is proposed for layout designs of 2D and 3D continua. As one of the most frequently used transforms in digital image compression, the DCT may significantly reduce the number of design variables in density-based topology optimization, and can hereby improve the efficiency of solving the topology optimization problems to a great extent. This way the DCT compression based density method (DCDM) could be quite attractive in the topology optimization of large-scale engineering structures where a huge number of design variables may present. Effectiveness and efficiency of the proposed method is demonstrated with several 2D and 3D examples including both mechanical and heat conduction problems. Through these examples, some interesting features of DCDM are revealed and discussed. Since high frequency components are inherently filtered in DCDM, there is no need to introduce additional density filter or sensitivity filter in the present model. It is shown by numerical examples that there is no sharp corners present in the final optimized layout obtained by DCDM, which is beneficial when considering the stress of structures.

IJARCCE - Computer and Communication Engineering

3 Abstract: Image Compression is a method, which reduces the amount of space required to store the image. The Discrete Cosine Transform (DCT) is a method that transforms a signal or image from spatial domain to frequency domain. This technique is widely used in image compression. In this paper, we present a lossless DCT compression technique for two-dimensional images. In several scenarios, the utilization of the presented technique for image compression results in comparable or better performance, when compared to the different modes of the lossless JPEG standard.

An image compression using using discrete cosine transforms and JPEG enconder

The proposed Multiresolution decomposition through wavelets transform of fractals coded images reveal strong relationships limit the frequency content .The research is includes other extensions of the wavelet transform and fractals are wavelet transform. And our work deals with deals with the JPEG Encoder for image compression upon comparing with Discrete cosine transform (DCT) is widely used in image processing, especially for compression. Some of the applications of two-dimensional DCT involve still image compression and compression of individual video frames.The JPEG standard has been an effective first solution to the standardization of image and the performance of DWT compression with DCT compression. And JPEG 2000 is a wavelet-based standard for the compression of still digital images The wavelet transform has emerged as a cutting edge technology, within the field of image compression. And Wavelet- based coding provides substantial improvements in picture quality at higher compression ratios.and also dicuss about of the proposed DCT based JPEG Encoder and Wavelet based JPEG Encoder.

JPEG DCT Compressiom

With the rapid spread of image processing applications and the further development of multimedia technologies, compression standards become more and more important. This paper intends to explain JPEG (Joint Photographic Experts Group) compression, which is currently a worldwide standard for digital image compression, is based on the discrete cosine transform (DCT). Based on the research, the paper describes theory and algorithms of the JPEG DCT compression and implements a baseline JPEG codec (encoder/decoder) with MATLAB.

QUALITY MEASURE OF THE COMPRESSED ECHO, X-RAY AND CT IMAGES

The paper reveals the analysis of the compression quality of true color medical images of echocardiogram (ECHO), X-radiation (X-ray) and computed tomography (CT) and further a comparison of compressed biomedical images of various sizes using two lossy compression techniques, set partitioning in hierarchical trees (SPIHT) and discrete cosine transform (DCT) to the original image is carried out. The study also evaluates the results after analyzing various objective parameters associated with the image. The objective of this analysis is to exhibits the effect of compression ratio on absolute average difference (AAD), cross correlation (CC), image fidelity (IF), mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity index measurement (SSIM) of the compressed image by SPIHT and DCT compression techniques. The results signify that the quality of the compressed image depends on resolution of the underlying structure where CT is found to be better than other image modalities. The X-ray compression results are equivalent by both the techniques. The compression results for large size biomedical images by SPIHT signifies that ECHO having comparable results to CT and X-ray while their DCT results are substandard. The compression results for comparatively smaller images of ECHO are not as good as X-ray and CT by both the compression techniques. The quality measurement of the compressed image has been designed using MATLAB.

Image Compression System using ANN

The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and high definition television (HDTV) has increased the need for effective and standardized image compression techniques. Among the emerging standards are JPEG, for compression of still images; MPEG, for compression of motion video; and CCITT H.261 (also known as Px64), for compression of video telephony and teleconferencing. All three of these standards employ a basic technique known as the discrete cosine transform (DCT), Developed by Ahmed, Natarajan, and Rao [1974]. Image compression using Discrete Cosine Transform (DCT) is one of the simplest commonly used compression methods. The quality of compressed images, however, is marginally reduced at higher compression ratios due to the lossy nature of DCT compression, thus, the need for finding an optimum DCT compression ratio. An ideal image compression system must yield high quality compressed images with good compression ratio, while maintaining minimum time cost. The neural network associates the image intensity with its compression ratios in search for an optimum ratio.

Zeros Removal with DCT Image Compression Technique

The discrete cosine transform (DCT) is a method for converting a signal into plain frequency components. It is extensively used in image compression. In this paper a new technique is proposed, namely ZRDCT (Zeros Removal with DCT) which is based on a lossy compression, and used to enhance image data compression.Image quality is measured impartially, using peak signal-to-noise ratio (PSNR) or picture quality scale, and individually using perceived image quality with compression factor (CF) being the main theme of this paper, taking into consideration the preservation of well PSNR outputs. The performance of DCT compression generally degrades low bit-rates mainly because of the underlying block-based DCT scheme. Experimental results demonstrated the effectiveness of the ZRDCT approach, which enhanced the performance of the conventional DCT image compression methods, by investigating and interrogating the whole image and hence enforcing mechanisms for finding possible redundant information and therefore the removal of unnecessary data which lead to an improvement in CF without upsetting PSNR.The new technique also proved to have low distortions with good quality PSNR, commendable CF and good execution time, when compared to other various DCT schemes and with some wavelet based image compression

Image auto-zoom technology for AFM automation

For the case of atomic force microscope (AFM) automation, we extract the most valuable sub-region of a given AFM image automatically for succeeding scanning to get the higher resolution of interesting region. Two objective functions are summarized based on the analysis of evaluation of the information of a sub-region, and corresponding algorithm principles based on standard deviation and Discrete Cosine Transform (DCT) compression are determined from math. Algorithm realizations are analyzed and two select patterns of sub-region: fixed grid mode and sub-region walk mode are compared. To speed up the algorithm of DCT compression which is too slow to practical applied, a new algorithm is proposed based on analysis of DCT’s block computing feature, and it can perform hundreds times faster than original. Implementation result of the algorithms proves that this technology can be applied to the AFM automatic operation. Finally the difference between the two objective functions is discussed with detail computations.

Steganographic data transmission robust against scaling attacks (resistant-to-scaling-attacks) and eliminating the hampering effect of signal-carrier

On the basis of analysis of steganographic system as a communications system with additional information, data transmission algorithms with modulation of the quantization index allowing for elimination of the hampering effect of a signal-carrier have been investigated. It was shown that the choice of quantization step enables us to effectively solve the trade-off task of building a stegosystem: distortions-rate-robustness, while the normalization of quantization step to the root-mean-squre value of signal-carrier in the coder and decoder eliminates the influence of constant multiplicative interference in the channel. Analitical relationships were obtained for the error probability in a channel with additive white noise. Recommendations regarding the selection of transformants of discrete cosine transform (DCT) for imformation embedding into static pictures subjected to the standardized procedure of JPEG DCT compression were given.