The thesis proposes using the efficient memory arrangement methods for the implementation of radix-r multi-dimensional Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT). By using the memory instead of the registers to buffer and reorder data, hardware complexity is significantly reduced. We use the recursive architecture that requires only one arithmetic-processing element to compute the entire DFT/DCT operation. The algorithm is based on efficient coefficient matrix factorization and data allocation. By exploiting the features of Kronecker product representation in the fast algorithm, the multi-dimensional DFT/DCT operation is converted into its corresponding 1-D problem and the intermediate data is stored in several memory units. In addition to the smaller area, we also propose a method to reduce the power consumption of the DFT/DCT processors.

Būtinybė apsaugoti autorinių teisių savininkus nuo neteisėto intelektinės nuosavybės naudojimosi ir platinimo vis didėja. Tam puikiai tinka skaitmeniniai vandens ženklai, kurie ne tik apsaugo nuo neleistino intelektinės nuosavybės naudojimosi, bet pasinaudojus vandens ženklais galima susekti neteisėtą vartotoją. Šiame darbe aptarta diskrečioji kosinuso transformacija (angl. Discrete Cosine Transform (DCT)) ir išplėsto spektro (angl. Spread-Spectrum) ženklinimo vandens ženklais metodai, plačiau išnagrinėtas DCT metodas ir patikrinta keletas paprastų vaizdo atakų, kaip antai: karpymas, suspaudimas, pasukimas ir pan. Dalies vaizdo iškirpimas, pasukimas gali neatpažįstamai pakeisti vandens ženklą. Taip nutinka, nes iš pakeisto paveikslėlio ištraukiant vandens ženklą priešingu įterpimui metodu pasikeičia vaizdo taškų koordinatės, o iškirpus dalies koordinačių trūksta. Priklausomai nuo posūkio kampo ar iškirptos dalies dydžio sugadinamas ir vandens ženklas, kuris nepasikeičia keičiant spalvą ar kontrastą. / The necessity to protect copyright owners from using and sharing illegal intellectual property still grows. Digital water marks fits perfectly for this matter. They not only prevent using illegal intellectual property, but they can also detect illegal user. In this paper the discrete cosine transform (DCT) and spread-spectrum water-marking methods were discussed, and DCT method was analysed, and several image attacks were tested, such as: trimming, compression, rotation and etc. The trimming a part of an image, rotation of an image can change water mark beyond recognition. It happens because taking water mark out from changed image using inverse method of insertion image pixel coordinates change, and trimming causes a missing of part of coordinates. Depending on the angle of rotation or the size of a trimmed part a water mark is corrupted, but it does not change when a colour or a contrast is being changed.

Η μείωση της κατανάλωσης ισχύος αποτελεί το πιο σημαντικό πρόβλημα στα ψηφιακά ηλεκτρονικά κυκλώματα. Διάφορες μέθοδοι έχουν προταθεί, μεταξύ αυτών η χρήση επεξεργαστών δυναμικά μεταβαλλόμενου μήκους λέξης. Με αυτόν τον τρόπο, στους υπολογισμούς που απαιτείται μέγιστη ακρίβεια ο επεξεργαστής μπορεί να χρησιμοποιεί το μέγιστο δυνατό μήκος λέξης, ενώ σε αυτούς που η χαμηλή κατανάλωση ισχύος είναι ο κύριος στόχος μπορεί να χρησιμοποιεί μικρότερο μήκος λέξης. Τέτοιες απαιτήσεις συναντούνται συχνά σε εφαρμογές ψηφιακής επεξεργασίας σήματος, όπως για παράδειγμα στην κωδικοποίηση εικόνας. Για το λόγο αυτό μελετήθηκε ο αντίστροφος διακριτός μετασχηματισμός συνημιτόνου, ο οποίος αποτελεί το πιο ενεργοβόρο κομμάτι στην κωδικοποίηση εικόνας και η σχέση της ακρίβειάς του με το μήκος λέξης του επεξεργαστή. Στη συνέχεια κατασκευάστηκαν οι δομικές μονάδες για τις αριθμητικές πράξεις του επεξεργαστή, αθροιστές, αφαιρέτες και πολλαπλασιαστές με δύο διαφορετικά μήκη λέξης και τέλος οι υπόλοιπες μονάδες του. Τα αποτελέσματα της σύνθεσής του δείχνουν ότι απαιτεί περισσότερες πύλες για την κατασκευή του από έναν αντίστοιχο σταθερού μήκους, όμως προσφέρει πολλά πλεονεκτήματα στη μείωση της κατανάλωσης. / Power saving is today's most important problem in digital circuits. Several methods have been proposed, including the use of a dynamically changing processor wordlength. With the adoption of this technique, calculations requiring maximum accuracy would use the maximum processor wordlength, while in those where low power is the main target a smaller wordlength could be used. Such requirements are frequently found in digital signal processing applications, such as image coding. Consequently, this diploma thesis studies the inverse discrete cosine transform, which is the most power-intensive part in image coding and the relation of its accuracy to the processor wordlength. After that, the structure of the blocks of the arithmetic and logic unit is explained, in order for the adders, subtracters and multipliers to be constructed with two different wordlengths and finally the remaining units of the processor are designed. The synthesis results show that this processor requires more gates. On the other hand, it offers many advantages in static and dynamic power reduction.

A biometric method is a more secure way of personal identification than passwords. This thesis examines the iris as a personal identifier with the use of neural networks as the classifier. A comparison of different feature extraction methods that include the Fourier transform, discrete cosine transform, the eigen analysis method, and the wavelet transform, is performed. The robustness of each method, with respect to distortion and noise, is also studied.

No description available.

Modified discrete cosine transform (MDCT) derived from DCT IV has emerged as the most suitable choice for transform domain audio coding applications due to its time domain alias cancellation property and de-correlation capability. In the present research work, we focus on MDCT domain analysis of audio signals for compression and other applications. We have derived algorithms for linear filtering in DCT IV and DST IV domains for symmetric and non-symmetric filter impulse responses. These results are also extended to MDCT and MDST domains which have the special property of time domain alias cancellation. We also derive filtering algorithms for the DCT II and DCT III domains. Comparison with other methods in the literature shows that, the new algorithm developed is computationally MAC efficient. These results are useful for MDCT domain audio processing such as reverb synthesis, without having to reconstruct the time domain signal and then perform the necessary filtering operations. In audio coding, the psychoacoustic model plays a crucial role and is used to estimate the masking thresholds for adaptive bit-allocation. Transparent quality audio coding is possible if the quantization noise is kept below the masking threshold for each frame. In the existing methods, the masking threshold is calculated using the DFT of the signal frame separately for MDCT domain adaptive quantization. We have extended the spectral integration based psychoacoustic model proposed for sinusoidal modeling of audio signals to the MDCT domain. This has been possible because of the detailed analysis of the relation between DFT and MDCT; we interpret the MDCT coefficients as co-sinusoids and then apply the sinusoidal masking model. The validity of the masking threshold so derived is verified through listening tests as well as objective measures. Parametric coding techniques are used for low bit rate encoding of multi-channel audio such as 5.1 format surround audio. In these techniques, the surround channels are synthesized at the receiver using the analysis parameters of the parametric model. We develop algorithms for MDCT domain analysis and synthesis of reverberation. Integrating these ideas, a parametric audio coder is developed in the MDCT domain. For the parameter estimation, we use a novel analysis by synthesis scheme in the MDCT domain which results in better modeling of the spatial audio. The resulting parametric stereo coder is able to synthesize acceptable quality stereo audio from the mono audio channel and a side information of approximately 11 kbps. Further, an experimental audio coder is developed in the MDCT domain incorporating the new psychoacoustic model and the parametric model.

This thesis deals with the development and evaluation of blind discrete cosine transform-based watermarking algorithms for copyright protection of digital still images using handwritten signatures and mobile phone numbers. The new algorithms take into account the perceptual capacity of each low frequency coefficients inside the Discrete Cosine Transform (DCT) blocks before embedding the watermark information. They are suitable for grey-scale and colour images. Handwritten signatures are used instead of pseudo random numbers. The watermark is inserted in the green channel of the RGB colour images and the luminance channel of the YCrCb images. Mobile phone numbers are used as watermarks for images captured by mobile phone cameras. The information is embedded multiple-times and a shuffling scheme is applied to ensure that no spatial correlation exists between the original host image and the multiple watermark copies. Multiple embedding will increase the robustness of the watermark against attacks since each watermark will be individually reconstructed and verified before applying an averaging process. The averaging process has managed to reduce the amount of errors of the extracted information. The developed watermarking methods are shown to be robust against JPEG compression, removal attack, additive noise, cropping, scaling, small degrees of rotation, affine, contrast enhancements, low-pass, median filtering and Stirmark attacks. The algorithms have been examined using a library of approximately 40 colour images of size 512 512 with 24 bits per pixel and their grey-scale versions. Several evaluation techniques were used in the experiment with different watermarking strengths and different signature sizes. These include the peak signal to noise ratio, normalized correlation and structural similarity index measurements. The performance of the proposed algorithms has been compared to other algorithms and better invisibility qualities with stronger robustness have been achieved.

This thesis presents a novel lip-reading approach to classifying utterances from video data, without evaluating voice signals. This work addresses two important issues which are • the efficient representation of mouth movement for visual speech recognition • the temporal segmentation of utterances from video. The first part of the thesis describes a robust movement-based technique used to identify mouth movement patterns while uttering phonemes. This method temporally integrates the video data of each phoneme into a 2-D grayscale image named as a motion template (MT). This is a view-based approach that implicitly encodes the temporal component of an image sequence into a scalar-valued MT. The data size was reduced by extracting image descriptors such as Zernike moments (ZM) and discrete cosine transform (DCT) coefficients from MT. Support vector machine (SVM) and hidden Markov model (HMM) were used to classify the feature descriptors. A video speech corpus of 2800 utterances was collected for evaluating the efficacy of MT for lip-reading. The experimental results demonstrate the promising performance of MT in mouth movement representation. The advantages and limitations of MT for visual speech recognition were identified and validated through experiments. A comparison between ZM and DCT features indicates that th e accuracy of classification for both methods is very comparable when there is no relative motion between the camera and the mouth. Nevertheless, ZM is resilient to rotation of the camera and continues to give good results despite rotation but DCT is sensitive to rotation. DCT features are demonstrated to have better tolerance to image noise than ZM. The results also demonstrate a slight improvement of 5% using SVM as compared to HMM. The second part of this thesis describes a video-based, temporal segmentation framework to detect key frames corresponding to the start and stop of utterances from an image sequence, without using the acoustic signals. This segmentation technique integrates mouth movement and appearance information. The efficacy of this technique was tested through experimental evaluation and satisfactory performance was achieved. This segmentation method has been demonstrated to perform efficiently for utterances separated with short pauses. Potential applications for lip-reading technologies include human computer interface (HCI) for mobility-impaired users, defense applications that require voice-less communication, lip-reading mobile phones, in-vehicle systems, and improvement of speech-based computer control in noisy environments.

Many scientific data sets are contaminated by noise, either because of data acquisition process, or because of naturally occurring phenomena. A first step in analyzing such data sets is denoising, i.e., removing additive noise from a noisy image. For images, noise suppression is a delicate and a difficult task. A trade of between noise reduction and the preservation of actual image features has to be made in a way that enhances the relevant image content. The beginning chapter in this thesis is introductory in nature and discusses the Popular denoising techniques in spatial and frequency domains. Wavelet transform has wide applications in image processing especially in denoising of images. Wavelet systems are a set of building blocks that represent a signal in an expansion set involving indices for time and scale. These systems allow the multi-resolution representation of signals. Several well known denoising algorithms exist in wavelet domain which penalize the noisy coefficients by threshold them. We discuss the wavelet transform based denoising of images using bit planes. This approach preserves the edges in an image. The proposed approach relies on the fact that wavelet transform allows the denoising strategy to adapt itself according to directional features of coefficients in respective sub-bands. Further, issues related to low complexity implementation of this algorithm are discussed. The proposed approach has been tested on different sets images under different noise intensities. Studies have shown that this approach provides a significant reduction in normalized mean square error (NMSE). The denoised images are visually pleasing. Many of the image compression techniques still use the redundancy reduction property of the discrete cosine transform (DCT). So, the development of a denoising algorithm in DCT domain has a practical significance. In chapter 3, a DCT based denoising algorithm is presented. In general, the design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated approach to design filters based on DCT is proposed in chapter 3. This algorithm reorganizes DCT coefficients in a wavelet transform manner to get the better energy clustering at desired spatial locations. An adaptive threshold is chosen because such adaptively can improve the wavelet threshold performance as it allows additional local information of the image to be incorporated in the algorithm. Evaluation results show that the proposed filter is robust under various noise distributions and does not require any a-priori Knowledge about the image. Inpainting is another application that comes under the category of image processing. In painting provides a way for reconstruction of small damaged portions of an image. Filling-in missing data in digital images has a number of applications such as, image coding and wireless image transmission for recovering lost blocks, special effects (e.g., removal of objects) and image restoration (e.g., removal of solid lines, scratches and noise removal). In chapter 4, a wavelet based in painting algorithm is presented for reconstruction of small missing and damaged portion of an image while preserving the overall image quality. This approach exploits the directional features that exist in wavelet coefficients in respective sub-bands. The concluding chapter presents a brief review of the three new approaches: wavelet and DCT based denoising schemes and wavelet based inpainting method.

We describe a fast method for the evaluation of an arbitrary high-dimensional multivariate algebraic polynomial in Chebyshev form at the nodes of an arbitrary rank-1 Chebyshev lattice. Our main focus is on conditions on rank-1 Chebyshev lattices allowing for the exact reconstruction of such polynomials from samples along such lattices and we present an algorithm for constructing suitable rank-1 Chebyshev lattices based on a component-by-component approach. Moreover, we give a method for the fast, exact and stable reconstruction.