Discrete Cosine Transform Application Research Articles

Circuit Design and Application of Discrete Cosine Transform Based on Memristor

Discrete Cosine Transformation(DCT) is an important method in image compression. With the advent of the era of big data, how to make efficient DCT calculation is the key to its application. Therefore, this paper proposes a one-step DCT calculation method from the point of view of analog circuit, which can greatly speed up the operation in the form of low energy consumption. Firstly, a new weight programming is proposed, and the negative weight problem is solved. On the basis of this analysis, One-dimensional DCT(1D-DCT) and One-dimensional IDCT(1D-IDCT) calculation circuit is proposed. After that, the One-dimensional circuit is designed into Two-dimensional circuit by using parallel processing. PSPICE simulation results show that the average calculation accuracy of the circuit is above 99%. Finally, the designed two-dimensional circuit is used in image compression, and it is found that the compression effect is significant in the case of high speed.

A novel approach for data hiding based on combined application of discrete cosine transform and coupled chaotic map

In the recent years chaotic map and Discrete Cosine transform (DCT) have surfaced to become an important field in data hiding methods. In this proposed work, a randomly high embedding chaotic value differencing method (RCVD) based on combined application of DCT and Chaotic map is proposed. DCT has energy compaction property which removes the rounding error problems in the embedding and extraction process. Whereas, chaotic maps have significant properties of sensitivity to initial and control parameters, which help to provide security to the proposed algorithm. The embedding of secret data is done by hybrid method namely randomly chaotic value differencing (RCVD). RCVD maintains the human vision system (HVS) by embedding more data in smooth area than in edge area. The experimental results are calculated in terms of Peak Signal to Noise Ratio (PSNR) and embedding capacity. The security of the proposed method is examined by the key space, key sensitivity parameter and histogram analysis. Additionally universal image quality index (Q) and entropy are also calculated for the proposed method. The evaluated results show that the proposed method outperforms the existing steganography techniques in terms of embedding capacity and PSNR.

A Robust Medical Images Watermarking Using FDCuT-DCT-SVD

One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.

Digital Image Watermarking Using Optimization and Encryption

In this paper, with the technique of chaotic encryption-based blind digital image watermarking, we introduce a new grey wolf optimization. Discrete cosine transform (DCT) is employed before embedding the watermark within the host image. The image at host is split into 8x8 which is a non-overlapping blocks before employing DCT application, and the watermark bit is included through modifying the differences between the neighboring blocks DCT coefficients. Then, grey wolf optimization algorithm (GWO) is employed. Arnold transform is used along with the chaotic encryption to provide additional watermark security. The presented model is applied to a collection of benchmark images and the results validated the performance in terms of PSNR. The presented method attains maximum PSNR value on entirely given test images.

Implementation of Modified Signed Discrete Cosine Transform

One of the most important properties of Discrete Cosine Transform (DCT) is high power compaction, due to this property DCT is used for coding, image processing, image compression etc. The DCT application can be speed up by Signed Discrete Cosine Transforms (SDCT). The SDCT is the modification approximates of the DCT. In this paper by proposing signum function we proposed a flow diagram of algorithm and its architecture by considering 8 point transforms.

Secure and Robust Digital Image Watermarking Using Coefficient Differencing and Chaotic Encryption

This paper presents a chaotic encryption-based blind digital image watermarking technique applicable to both grayscale and color images. Discrete cosine transform (DCT) is used before embedding the watermark in the host image. The host image is divided into $8\times 8$ nonoverlapping blocks prior to DCT application, and the watermark bit is embedded by modifying difference between DCT coefficients of adjacent blocks. Arnold transform is used in addition to chaotic encryption to add double-layer security to the watermark. Three different variants of the proposed algorithm have been tested and analyzed. The simulation results show that the proposed scheme is robust to most of the image processing operations like joint picture expert group compression, sharpening, cropping, and median filtering. To validate the efficiency of the proposed technique, the simulation results are compared with certain state-of-art techniques. The comparison results illustrate that the proposed scheme performs better in terms of robustness, security, and imperceptivity. Given the merits of the proposed scheme, it can be used in applications like e-healthcare and telemedicine to robustly hide electronic health records in medical images.

Investigation of a novel common subexpression elimination method for low power and area efficient DCT architecture.

A wide interest has been observed to find a low power and area efficient hardware design of discrete cosine transform (DCT) algorithm. This research work proposed a novel Common Subexpression Elimination (CSE) based pipelined architecture for DCT, aimed at reproducing the cost metrics of power and area while maintaining high speed and accuracy in DCT applications. The proposed design combines the techniques of Canonical Signed Digit (CSD) representation and CSE to implement the multiplier-less method for fixed constant multiplication of DCT coefficients. Furthermore, symmetry in the DCT coefficient matrix is used with CSE to further decrease the number of arithmetic operations. This architecture needs a single-port memory to feed the inputs instead of multiport memory, which leads to reduction of the hardware cost and area. From the analysis of experimental results and performance comparisons, it is observed that the proposed scheme uses minimum logic utilizing mere 340 slices and 22 adders. Moreover, this design meets the real time constraints of different video/image coders and peak-signal-to-noise-ratio (PSNR) requirements. Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively.

A Probabilistic Estimation Bias Circuit for Fixed-Width Booth Multiplier and Its DCT Applications

In this brief, a probabilistic estimation bias (PEB) circuit for a fixed-width two's-complement Booth multiplier is proposed. The proposed PEB circuit is derived from theoretical computation, instead of exhaustive simulations and heuristic compensation strategies that tend to introduce curve-fitting errors and exponential-grown simulation time. Consequently, the proposed PEB circuit provides a smaller area and a lower truncation error compared with existing works. Implemented in an 8 × 8 2-D discrete cosine transform (DCT) core, the DCT core using the proposed PEB Booth multiplier improves the peak signal-to-noise ratio by 17 dB with only a 2% area penalty compared with the direct-truncated method.

Morphological representation of DCT coefficients for image compression

Recent success in discrete cosine transform (DCT) image coding is mainly attributed to recognition of the importance of data organization and representation. Currently, there are several competitive DCT-based coders such as DCT-based embedded image coding (EZDCT) (see Xiong et al., Z., 1996) and significance tree quantization (STQ) (see Davis, G. and Chawla, S., 1997). In the wavelet context, morphological representation of wavelet data has achieved the best compression performance. The representatives are morphological representation of wavelet data (MRWD) (see Servetto, S. et al., 1999) and significance-linked connected component analysis (see Chai, B.-B. et al., 1999). We show that, by proper reorganization of its coefficients, the block-based DCT can have similar characteristics, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc., to the wavelet transform. These characteristics can widen DCT applications relevant to image compression, image retrieval, pattern recognition, etc. We then present an image coder utilizing these characteristics by morphological representation of DCT coefficients (MRDCT). The experiments show that MRDCT is among the state-of-the-art DCT-based image coders reported in the literature. For example, for the Lena image at 0.25 bpp, MRDCT outperforms JPEG, STQ and EZDCT in peak signal-to-noise ratio by 1.0, 1.0, and 0.3 dB, respectively.

DCTlab: Educational Software for Still Image Compression and its Application in a Digital Television Course

Current standards for the compression of still and moving images use Discrete Cosine Transform (DCT) to remove spatial redundancy in images. Students specialising in image and video system engineering need to know why DCT is important in their field of interest and to understand the influence of DCT-based image compression on picture quality. Therefore, we have developed educational software, called DCTlab, that helps students to analyse DCT application in still image compression systems. This paper describes software characteristics, its application in a digital television course and learning outcomes.

A bit-rate reduction for MUSE signal

A novel bit-rate reduction method developed for the MUSE (multiple subNyquist sampling encoding) signal is discussed. This method uses an orthogonal transformation by which the MUSE data-rate is reduced from 1/2 to 1/3 of the original, maintaining a picture quality acceptable for the home-use digital VTR of a HDTV (high-definition television) system. By simultaneous application of two-dimensional DCT (discrete cosine transform) and nonlinear quantization methods for a quincuncial pixel arrangement formed within a field, an optimum data assignment between DCT coefficients and nonlinear quantization values is accomplished by using a variable length coding method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>