Narrow Results

In this paper, a facial expression recognition system based on cerebella model articulation controller with a clustering memory (CMAC-CM) is presented. Firstly, the facial expression features were automatically preprocessed and extracted from given still images in the JAFFE database in which the frontal view of faces were contained. Next, a block of lower frequency DCT coefficients was obtained by subtracting a neutral image from a given expression image and rearranged as input vectors to be fed into the CMAC-CM that can rapidly obtain output using nonlinear mapping with a look-up table in training or recognizing phase. Finally, the experimental results have demonstrated recognition rates with various block sizes of coefficients in lower frequency and cluster sizes of weight memory. A mean recognition rate of 92.86% is achieved for the testing images. CMAC-CM takes 0.028 seconds for test image in testing phase.

In this paper, a steganography algorithm has been proposed which is based on quantization table modification and image scrambling in the DCT domain. First, the algorithm homogenizes the energy by scrambling the cover image to improve the number of DCT coefficients suitable for information embedding. Second, the embedding capacity of the DCT block is determined by the value of the quantization table in the algorithm, different quantization tables get different embedding effects. In addition, this paper proposes an optimized modified quantization table. Extensive experiments show that the proposed algorithm achieves a great potential for confidential data and indiscernible image quality.

Current multimedia design processes suffer from the excessively large time spent on testing new IP-blocks with references based on large video encoders specifications (usually several thousands lines of code). The appropriate testing of a single IP-block may require the conversion of the overall encoder from software to hardware, which is difficult to complete in the short time required by the competition-driven reduced time-to-market demanded for the adoption of a new video coding standard. This paper presents a new design flow to accelerate the conformance testing of an IP-block using the H.264/AVC software reference model. An example block of the simplified 8 × 8 transformation and quantization, which is adopted in FRExt, is provided as a case study demonstrating the effectiveness of the approach.

In this paper, a novel neural technique is proposed for FET small-signal modeling. This technique is based on the discrete cosine transform (DCT) and the Mel-frequency cepstral coefficients (MFCCs). The input data to traditional neural systems for FET small-signal modeling are the scattering parameters and the corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed technique, the input data are considered random, and the MFCCs are calculated from these inputs and their DCT. The MFCCs are used to give a few features from the input random data sequence to be used for the training of the neural networks. The objective of using MFCCs is to characterize the random input sequence with features that are robust against measurement errors. The MFCCs extracted from the DCT of the inputs increase the robustness against measurement errors. There are two benefits that can be achieved using the proposed technique; a reduction in the number of neural inputs and hence a faster convergence of the neural training algorithm and a robustness against measurement errors in the testing phase. Experimental results show that the technique based on the DCT and MFCCs is less sensitive to measurement errors than using the actual measured scattering parameters.

This paper presents non-recursive computation equation for 8 × 8 two-dimensional (2D) discrete cosine transform (DCT) along with novel VLSI architecture for direct computation of DCT without transposition memory. All intermediate operations are performed in non-fraction format. Being non-recursive and all intermediate calculations free of fractions, the proposed architecture has excellent accuracy in terms of peak signal-to-noise ratio (PSNR). The architecture is implemented in 0.18-μm CMOS standard cell technology library and prototyped in Field programmable gate array (FPGA) technology for the silicon validation. Implementation result shows that it has low power consumption and low area as compared to the available architectures of 2D DCT. To further increase the accuracy of computations, hardware overhead is very less as only one register and a multiplier bit-widths need to be changed.

Multiplier-free fast algorithms are derived and analyzed for realizing the 8-point discrete sine transform of type II and type VII (DST-II and DST-VII) transforms with applications in image and video compression. A new fast algorithm is identified using numerical search methods for approximating DST-VII without employing multipliers. In addition, recently proposed fast algorithms for approximating the 8-point DCT-II are now extended to approximate DST-II. All proposed approximations for DST-II and DST-VII are compared with ideal transforms, and circuit complexity is measured using FPGA-based rapid prototypes on a 90nm Xilinx Virtex-4 device. The proposed architectures find applications in emerging video processing standards such as H.265/HEVC.

COrdinate Rotation DIgital Computer (CORDIC) is commonly utilized for the computation of cosine/sine i.e., the trigonometric functions, singular value decomposition, in digital signal processing (especially in image/video processing), etc. This paper introduces an energy efficient quality tunable CORDIC architecture that computes the cosine/sine values of any required angle in real-time, and is thus well suited for real time DSP applications, especially for image or video processing applications. The proposed architecture reduces the latency and overcomes data dependency by simultaneously performing all the five iterations, that may vary depending upon the desired energy efficiency. The novelty of this architecture is that, desired quality can be achieved by selecting one out of the available three modes. In order to assess the efficacy of the suggested architecture, some benchmark images are processed using the Discrete Cosine Transform (DCT) coefficients obtained via the proposed design. Energy saving is achieved at the cost of slight acceptable degradation in the output image quality. Further, the simulation results show that the proposed architecture is 92.3%, 2.8% and 49.08% more energy efficient than the existing basic, scale-free and lookahead CORDIC architectures, respectively.

The air temperature is a vital measuring variable widely applied in science and engineering as it is considered one vital part of the lives of plants and animals. From the point of view of time series analysis and data-driven modeling, changes in daily 24h air temperature can be described as one time-series data. Compared to relatively complicated models, simple, efficient and of even better performance are advantages of data-driven-based concise modeling methods, especially for embedded applications which usually have limited resources such as low RAM and low processing power. This study takes the perspective of data-driven modeling and time-series analysis to present one succinct and efficient solution based on discrete cosine transform (DCT)-extended modeling for the hourly air temperature forecast (HTF) in engineering and embedded applications. Characteristics of the DCT lie in optimal de-correlation and energy compaction. Orienting upon extended-DCT modeling, one concise least-squares (LS)-extended DCT predictive algorithm for the HTF is introduced. Then one Mbed-based embedded system design utilizing the proposed LS-extended DCT predictive algorithm as well as theoretical analyses and fast computation of arbitrary length DCT is presented. Verification results indicate the suitability of the proposed predictive algorithm. Only the temperature data without other parameters being straightly employed in forecast modeling is one major advantage of the proposed data-driven DCT-based HTF method, which benefits it to be suitable for engineering and embedded applications.

In the field of secure communications, the robustness of cipher images transmitted in various channels is becoming increasingly important. In this paper, a robust image encryption algorithm combining a new chaotic system and discrete cosine transform is proposed, which is interlinked with plain information and is resistant to high-intensity noise attacks. First, a 5D continuous hyperchaotic system is proposed, leading to an interrelated sequence of five chaotic sequences. Second, the plain image is subjected to discrete cosine transform. Then the transform domain image is quantized, and some high-frequency components are removed, and then the high-frequency components are filled with chaotic sequences. Next, the transform domain image is scrambled, and inverse discrete cosine transform is performed, and its gray value is mapped to obtain a spatial domain image. Finally, the spatial image is scrambled by the spiral transformation, and then the diffusion operation is performed to obtain the encrypted image. Through the simulation experiment, the histogram, correlation, differential attack, and robustness are analyzed. The experimental results show that the proposed encryption algorithm can resist high-intensity noise attacks and has good encryption performance.

It is an important problem nowadays to ensure the security of encrypted multimedia contents on the Internet. In this paper, a new blind attack procedure called NZCA is proposed to decrypt images that are encrypted by DCT-based methods. It threatens almost all existing DCT-based image encryption methods without the need of knowing the encryption algorithm details. The parameters of the NZCA can be adaptively selected via unsupervised learning with spectrum analysis of image projection. The effectiveness of the NZCA has been quantitatively evaluated by comparing the decrypted images with the edge sketches of original images. Our experiments on a number of real-world images show that NZCA is very powerful. Furthermore, we point out that a special form of inter-block shuffle, full inter-block shuffle (FIBS), is immune to the NZCA. Hence we suggest that all DCT-based image encryption methods for JPEG, MPEG, H.26x, etc. employ the FIBS so as to enhance the security.

Most of image compression methods are based on frequency domain transforms that are followed by a quantization and rounding approach to discard some coefficients. It is obvious that the quality of compressed images highly depends on the manner of discarding these coefficients. However, finding a good balance between image quality and compression ratio is an important issue in such manners. In this paper, a new lossy compression method called linear mapping image compression (LMIC) is proposed to compress images with high quality while the user-specified compression ratio is satisfied. This method is based on discrete cosine transform (DCT) and an adaptive zonal mask. The proposed method divides image to equal size blocks and the structure of zonal mask for each block is determined independently by considering its gray-level distance (GLD). The experimental results showed that the presented method had higher pick signal to noise ratio (PSNR) in comparison with some related works in a specified compression ratio. In addition, the results were comparable with JPEG2000.

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.

It is well known that the distribution of the discrete cosine transform (DCT) coefficients of most natural images follows a Laplace distribution. In this note, using a Bayesian approach, we derive two popular models for the distribution of the actual DCT coefficient. We illustrate the superior performance of these models over the standard Laplace model.

Digital watermarking is a process of embedding hidden information called watermark into different kinds of media objects. It uses basic modulation, multiplexing and transform techniques of communication for hiding information. Traditional techniques used are least significant bit (LSB) modification, discrete cosine transform (DCT), discrete wavelet transform (DWT), discrete Fourier transform (DFT), code division multiple access (CDMA) or a combination of these. Among these, CDMA is the most robust against different attacks except geometric attacks. This paper proposes a blind and highly robust watermarking technique by utilizing the basis of orthogonal frequency division multiplexing (OFDM) and CDMA communication system. In this scheme, the insertion process starts by taking DFT of host images, permuting the watermark bits in randomized manner and recording them in a seed as a key. Then PSK modulation follows inverse DFT (IDFT) that gives watermark information as OFDM symbols. These symbols are spread using spreading codes and then arithmetically added to the host image. Finally, scheme applies inverse DCT (IDCT) to get watermarked host images. The simulation results of the proposed scheme are compared with CDMA-based scheme in DCT domain. The results show that the robustness of the proposed scheme is higher than the existing scheme for non-geometric attacks.

The recording of electrical activity of the heart by using electrodes is known as electrocardiography (ECG). In long time monitoring of ECG, a huge amount of data needs to be handled. To handle the situation, an efficient compression technique which can retain the clinically important features of ECG signal is required. The continuous monitoring of this signal requires a large amount of memory. Hence, there is a requirement of compression. The compression of ECG signal using transforms in cascade is explored to incorporate the added advantages of both the transforms. This paper presents compression of ECG signal by hybrid technique consisting of cascade and parallel combination of discrete cosine transform (DCT) and discrete wavelet transform (DWT). The simulation is carried out using MATLAB tool. Various wavelet transforms are used for the testing purpose. The performance measures used are Percent square mean Root Difference (PRD) and CR to validate the results. The methodology using cascade combination proved to be better than the parallel technique in terms of Compression Ratio (CR). The highest CR achieved is 28.2 in the method using DCT and DWT in cascade. Different DWTs are used for the testing purpose. The parallel method shows the improved PRD as compared to the cascade method.

In this chapter, we describe several fixed length coding schemes based on trellis coded quantization (TCQ) that have been developed for image transmission over noisy channels such as wireless links. In general, wireless channels are time-varying, bandwidth constrained, and error pruned. Therefore, it is desired that the images are not only appropriately coded to achieve bandwidth compression, but also coded in such a way that the coded bitstream is resilient to channel errors and robust to time-varying channel distortion and fading. The proposed schemes consist of the basic algorithm of uniform threshold TCQ (UTTCQ) and two enhanced schemes with increased computational complexity. The enhanced schemes include nonuniform threshold TCQ and UTTCQ with block classification. We demonstrate that, in the absence of channel coding, the proposed fixed length coding schemes can be designed to achieve efficient compression, error resilience, and robustness. In particular, the fixed length coding schemes will not suffer from the loss of synchronization that often causes catastrophic error effects when more efficient variable length source coding is adopted. We also present a scheme of layered transmission with unequal error protection channel coding to further improve the performance of image transmission over noisy channels.

The basic unit of image coding based on DCT was deemed as a complete entity, such as 8 × 8 code block in JPEG standard, therefore, the image coding based on DCT has large difficulty to implement resolution scalability. This paper proposed a resolution scalability scheme for image codec based on DCT. The encoder recombines the DCT coefficients according to their frequency, and the sub DCT code blocks are encoded in term of the image resolution. After decoding the coefficients form low-frequency to high-frequency, the decoder selects the IDCT models to reconstruct the image from low resolution to high resolution. Experimental results show that our method can get more flexible scalability than that of the methods of DWT base.