Browsing by Subject "Discrete cosine transforms"

Now showing 1 - 7 of 7

Open Access
Compression of images in CFA format
(IEEE, 2006) Cüce, Halil İbrahim; Çetin, A. Enis; Davey, M. K.
In this paper, images in Color Filter Array (CFA) format are compressed without converting them to full-RGB color images. Green pixels are extracted from the CFA image data and placed in a rectangular array, and compressed using a transform based method without estimating the corresponding luminance values. In addition, two sets of color difference (or chrominance) coefficients are obtained corresponding to the red and blue pixels of the CFA data and they are also compressed using a transform based method. The proposed method produces better PSNR values compared to the standard approach of bilinear interpolation followed by compression.
Open Access
Empirical mode decomposition aided by adaptive low pass filtering
(IEEE, 2012) Öztürk, Onur; Arıkan, Orhan; Çetin, A. Enis
Empirical Mode Decomposition (EMD) is an adaptive signal analysis technique which derives its basis functions from the signal itself. EMD is realized through successive iterations of a sifting process requiring local mean computation. For that purpose, local minima and maxima of the signal are assumed to constitute proper local time scales. EMD lacks accuracy, however, experiencing the so-called mode mixing phenomenon in the presence of noise which creates artificial extrema. In this paper, we propose adaptively filtering the signal in Discrete Cosine Transform domain before each local mean computation step to prevent mode mixing. Denoising filter thresholds are optimized for a product form criterion which is a function of the preserved energy and the eliminated number of extrema of the signal after filtering. Results obtained from synthetic signals reveal the potential of the proposed technique. © 2012 IEEE.
Open Access
JPEG hardware accelerator design for FPGA
(IEEE, 2007) Duman, Kaan; Çoǧun, Fuat; Öktem, L.
A fully pipelined JPEG hardware accelerator that runs on FPGA is presented. The accelerator is designed interactively in a simulation environment, using a DSP hardware design automation tool chain. The encoder part of the accelerator accepts 8×8 image blocks in a streaming fashion, and outputs the zigzag-scanned, quantized 2-D DCT coefficients of the block. The decoder part accepts zigzag-scanned, quantized DCT coefficients, and outputs reconstructed 8×8 image block. Each part has a throughput of one system clock per pixel per channel. The encoder employs a fast pipelined implementation for 2-D DCT [1]. For the decoder, a new pipelined 2-D IDCT structure is developed. Our IDCT structure is based on an IDCT factorization for software implementation [2], and is inspired by the pipelined DCT structure employed in the encoder. The resource utilization and maximum frequency figures for a particular FPGA target suggest that our accelerator has competitive performance.
Open Access
Moving region detection in compressed video
(Springer, 2004) Töreyin, B. U.; Çetin, A. Enis; Aksay, A.; Akhan, M. B.
In this paper, an algorithm for moving region detection in compressed video is developed. It is assumed that the video can be compressed either using the Discrete Cosine Transform (DOT) or the Wavelet Transform (WT). The method estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference. The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. In the case of DOT compressed video, the DC values of 8 by 8 image blocks of Y, U and V channels are used for estimating the background scene. This leads to a computationally efficient method and a system compared to the existing motion detection methods. © Springer-Verlag 2004.
Open Access
Recognition of vessel acoustic signatures using non-linear teager energy based features
(IEEE, 2016-10) Can, Gökmen; Akbaş, Cem Emre; Çetin, A. Enis
This paper proposes a vessel recognition and classification system based on vessel acoustic signatures. Teager Energy Operator (TEO) based Mel Frequency Cepstral Coefficients (MFCC) are used for the first time in Underwater Acoustic Signal Recognition (UASR) to identify platforms the acoustic noise they generate. TEO based MFCC (TEO-MFCC), being more robust in noisy conditions than conventional MFCC, provides a better estimation platform energy. Conventionally, acoustic noise is recognized by sonar oper-ators who listen to audio signals received by ship sonars. The aim of this work is to replace this conventional human-based recognition system with a TEO-MFCC features-based classification system. TEO is applied to short-time Fourier transform (STFT) of acoustic signal frames and Mel-scale filter bank is used to obtain Mel Teager-energy spectrum. The feature vector is constructed by discrete cosine transform (DCT) of logarithmic Mel Teager-energy spectrum. Obtained spectrum is transformed into cepstral coefficients that are labeled as TEO-MFCC. This analysis and implementation are carried out with datasets of 24 different noise recordings that belong to 10 separate classes of vessels. These datasets are partially provided by National Park Service (NPS). Artificial Neural Networks (ANN) are used as a classification method. Experimental results demonstrate that TEO-MFCC achieves 99.5% accuracy in classification of vessel noises. © 2016 IEEE.
Open Access
Teager energy based feature parameters for robust speech recognition in car noise
(IEEE, Piscataway, NJ, United States, 1999) Jabloun, F.; Çetin, A. Enis
In this paper, a new set of speech feature parameters based on multirate signal processing and the Teager Energy Operator is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by log-compression and inverse DCT computation. The new feature parameters have a robust speech recognition performance in car engine noise which is low pass in nature.
Open Access
Wavelet domain textual coding of Ottoman script images
(SPIE, 1996-03) Gerek, Ömer. N.; Çetin, A. Enis; Tewfik, A. H.
Image coding using wavelet transform, DCT, and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters This method efficiently compresses document images and is suitable for database research, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that are described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.