Hybrid Discrete Cosine Transform Research Articles

Hybrid DCT-SVD Based Digital Watermarking Scheme with Chaotic Encryption for Medical Images

The quality of medical image is crucial due to the sensitivity of medical diagnoses. Nowadays medical images are commonly shared between various entities across the medical sector to improve the quality of diagnoses. Thus, watermarking process is usually applied on these images to embed patient’s private information. One of the key challenges is preserving image quality, keeping it clean of distortions after the watermarking process, and sustaining robustness. In this work, a hybrid Discrete Cosine Transform (DCT) and Singular value decomposition (SVD) based image watermarking scheme with chaotic encryption is proposed. The obtained results have demonstrated that the proposed scheme is secure and robust compared to the present schemes. In particular, the results show decent robustness and excellent imperceptibility of watermarked host medical images. Moreover, Peak signal to noise ratio (PSNR), and Normalized Correlation (NC) are used to evaluate the performance of the scheme. The scheme has shown a minimum PSNR of 59 dB and an NC value of 1 with no attacks applied, and not less than 0.5 with the presence of various attacks.

A novel hybrid DCT and DWT based robust watermarking algorithm for color images

In this paper, a novel robust color image watermarking method based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. In this method, RGB cover image is divided into red, green and blue components. DCT and DWT are applied to each color components. Grayscale watermark image is scrambled by using Arnold transform. DCT is performed to the scrambled watermark image. Transformed watermark image is then divided into equal smaller parts. DCT coefficients of each watermark parts are embedded into four DWT bands of the color components of the cover image. The robustness of the proposed color image watermarking has been demonstrated by applying various image processing operations such as rotating, resizing, filtering, jpeg compression, and noise adding to the watermarked images. Experimental results show that the proposed method is robust to the linear and nonlinear attacks and the transparency of the watermarked images has been protected.

An Enhancement of Data Hiding Imperceptibility using Slantlet Transform (SLT)

This study proposes a hybrid technique in securing image data that will be applied in telemedicine in future. Based on the web-based ENT diagnosis system using Virtual Hospital Server (VHS), patients are able to submit their physiological signals and multimedia data through the internet. In telemedicine system, image data need more secure to protect data patients in web. Cryptography and steganography are techniques that can be used to secure image data implementation. In this study, steganography method has been applied using hybrid between Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) technique. DCT is calculated on blocks of independent pixels, a coding error causes discontinuity between blocks resulting in annoying blocking artifact. While SLT applies on entire image and offers better energy compaction compare to DCT without any blocking artifact. Furthermore, SLT splits component into numerous frequency bands called sub bands or octave bands. It is known that SLT is a better than DWT based scheme and better time localization. Weakness of DCT is eliminated by SLT that employ an improved version of the usual Discrete Wavelet Transform (DWT). Some comparison of technique is included in this study to show the capability of the hybrid SLT and DCT. Experimental results show that optimum imperceptibility is achieved.

Novel Image PreprocessingApproach for Automatic Speech Recognition

This research is intending to provide a novel approach of manipulating automatic speech recognition using image recognition approach. This research introduces hybrid 2D-Image-Hidden Markov Model(2DI)-(HMM) approach to handle preprocessing classification task in Automatic Speech Recognition System (ASR). The focus in this research is in the classification task. Due to that the proposed approach is novel and is a task in the whole ASR, it is evaluated using relative comparison to other popular approaches to run the same task on the same database. The relative comparison with hybrid Gaussian Mixture (GMM)-HMM with Mel Frequency Cepstral (MFCC) features is considered as reference results. This research introduces a new method of mapping speech signal into two-dimensionalspace. Speech stream is segmented and then the frequency contents are projected into frequency domain using a balanced tree structure filter. The wavelet packets technique is used to implement the filtering. The tree structure is captured into image. Database is constructed of encoded images. The imagesthenare segregated into speech classes. Hybrid Discrete Cosine Transform (DCT) based featuresare used for image encoding with (HMM) as Class model is evaluated against MFCC-HMM for the same classification problem. The proposed hybrid model indicates better balanced results over MFCC-HMM for handling the different classes. The considered classes in this research are vowels, consonants, plosives and speech silence.KED-TIMITCorpus is used in this research as source of speech information. This approach is indicating promising results especiallyin Silence and vowels detection.

Hybrid Wavelet and Discrete Cosine Transform Methods for Ethnicity Identification

Ethnic (Race) denotes to people who share common facial features that perceptually discriminate them from members of other ethnic groups. Ethnicity identification from face images is a process of facial features compilation of an individual compared to existing faces to inference his/her ethnic class, it play important role in face-related applications. In this paper, an improved ethnicity identification system’s accuracy is proposed through hybrid Wavelet and DCT (Discrete Cosine Transform) global feature extractor. Firstly, a wavelet transform is applied to the face image with 4 levels of decomposition. And then the DCT transform is performed on the LL4 (Low-Low) approximation coefficients band. A frontal facial database containing (950) images of three different ethnic groups (European, Oriental, and African) with different conditions (lighting, expression, with glasses or without) is used for experimental tests. Results show that the proposed scheme is outperform other recent related works in terms of accuracy and efficiency.

Lossy Compression Schemes Based on Transforms-A Literature Review on Medical Images

In recent years, advances in information technology and telecommunications have played a crucial role as catalysts for significant developments in the sector of healthcare. These technological advances have played a very strong role in the field of medical imaging. The number of digital m edical images has increased rapidly on the Internet. The necessity of fast and secure diagnosis is vital in the medical world.The purpose of medical image compression is express medical images with less data so that it can be efficiently stored and transm itted. Diagnosis is effective only when compression techniques preserve all the relevant and important image information needed. There are basically two types of image compression: lossless and lossy. Lossless coding does not permit high compression ratios where as lossy achieve high compression ratio. Among the existing lossy compression schemes, transform coding is one of the most effective strategies. This paper summarizes the different compression methods based on transforms like Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT), Hybrid DCT -DWT and Contourlet transform. And it has been analyzed that Contourlet transform have superior overall performance over other transforms in terms of PSNR.

A Comparative Study of Image Compression Algorithms

Digital images in their uncompressed form require an enormous amount of storage capacity. Such uncompressed data needs large transmission bandwidth for the transmission over the network. Discrete Cosine Transform (DCT) is one of the widely used image compression method and the Discrete Wavelet Transform (DWT) provides substantial improvements in the quality of picture because of multi resolution nature. Image compression reduces the storage space of image and also maintains the quality information of the image. In this research study the performance of three most widely used techniques namely DCT, DWT and Hybrid DCT-DWT are discussed for image compression and their performance is evaluated in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR). The experimental results obtained from the study shows that the Hybrid DCT- DWT technique for image compression has in general a better performance than individual DCT or DWT. .

Impact Analysis for Securing Image Data using Hybrid SLT and DCT

This paper propose a hybrid technique in securing image data that will be applied in telemedicine in future. Based on the web-based ENT diagnosis system using Virtual Hospital Server (VHS), patients are able to submit their physiological signals and multimedia data through the internet. In telemedicine system, image data need more secure to protect data patients in web. Cryptography and steganography are techniques that can be used to secure image data implementation. In this paper, steganography method is suggested to be applied using hybrid between Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) technique. DCT is calculated on blocks of independent pixels, a coding error causes discontinuity between blocks resulting in annoying blocking artifact. While SLT applies on entire image and offers better energy compaction compare to DCT without any blocking artifact. Furthermore, SLT splits component into numerous frequency bands called sub bands or octave bands. It is known that SLT is a better than DWT based scheme and better time localization. Weakness of DCT is eliminated by SLT that employ an improved version of the usual Discrete Wavelet Transform (DWT). Some comparison of technique is included in this paper to show the capability of the hybrid SLT and DCT.

A hybrid M-channel filter bank and DCT framework for H.264/AVC intra coding

In H.264/AVC, discrete cosine transform (DCT) is performed on the residual blocks after prediction. However, the mismatch between variable block sizes and the fixed transform matrix not only degrades decorrelation performance but also causes severe blocky artifacts inside the blocks. In previous work, M-channel filter bank system (MCFBS) was proposed to overcome these defects. However, the increased percentage of encoding time by using MCFBS is very high, especially for intra coding. More seriously, the constructed M-channel filter bank with floating-point coefficients is an obstacle to hardware implementation. In this work, a hybrid M-channel Filter bank and DCT (HMD) framework is proposed for intra coding. Besides, the integer transform of a newly constructed M-channel filter bank is also implemented for HMD. Experimental results demonstrate that HMD can reduce 64---69% of the complexity of MCFBS with negligible quality degradation.

Quality and Power Efficient Architecture for the Discrete Cosine Transform

In recent years, the demand for multimedia mobile battery-operated devices has created a need for low power implementation of video compression. Many compression standards require the discrete cosine transform (DCT) function to perform image/video compression. For this reason, low power DCT design has become more and more important in today's image/video processing. This paper presents a new power-efficient Hybrid DCT architecture which combines Loeffler DCT and binDCT in terms of special property on luminance and chrominance difference. We use Synopsys PrimePower to estimate the power consumption in a TSMC 0.25-μm technology. Besides, we also adopt a novel quality assessment method based on structural distortion measurement to measure the quality instead of peak signal to noise rations (PSNR) and mean squared error (MSE). It is concluded that our Hybrid DCT offers similar quality performance to the Loeffler, and leads to 25% power consumption and 27% chip area savings.