By Topic

Vision, Image and Signal Processing, IEE Proceedings -

Issue 5 • Date 30 Oct. 2004

Filter Results

Displaying Results 1 - 13 of 13
  • Design and VLSI implementation of QMF banks

    Publication Year: 2004 , Page(s): 421 - 427
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (430 KB)  

    The authors consider the design of multirate filterbanks for applications such as subband coding with IIR QMF (quadrature mirror filter) pairs. These offer reduced complexity and low latency at the expense of the loss of exact linear phase. In particular, consideration is given to the use of all-pass sections where linear phase is approximately achieved by being part of the objective in numerical optimisation experiments. This approach compares favourably with previous IIR based approaches. Finite wordlength design using simulated annealing shows that low coefficient wordlength may be used. This leads to efficient realisations with three-port adaptors. Using pipelining implementation, a flexible VLSI architecture is designed that can be used for a variety of subband decompositions. Layout and simulation of the design have been performed. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • GA-based DCT quantisation table design procedure for medical images

    Publication Year: 2004 , Page(s): 353 - 359
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (497 KB)  

    Medical images are widely used in the diagnosis of diseases. These imaging modalities include computerised tomography (CT), magnetic resonance imaging (MRI), ultrasonic (US) imaging, X-radiographs, etc. However, medical images have large storage requirements when high resolution is demanded; therefore, they need to be compressed to reduce the data size so as to achieve a low bit rate for transmission or storage, while maintaining image information. The Joint Photographic Experts Group (JPEG) developed an image compression tool that is one of the most widely used products for image compression. One of the factors influencing the performance of JPEG compression is the quantisation table. The bit rate and the decoded quality are determined simultaneously by the quantisation table, and therefore, the table has a strong influence on the whole compression performance. The author aims to provide a design procedure to seek sets of better quantisation parameters to raise the compression performance to achieve a lower bit rate while preserving high decoded quality. A genetic algorithm (GA) was employed to promote higher compression performance for medical images. The goal was to develop a design procedure to find quantisation tables that contribute to better compression efficiency in terms of bit rate and decoded quality. Simulations were carried out on different kinds of medical images. Resulting experimental data demonstrate that the GA-based search procedures can generate better performance than JPEG 2000 and JPEG even though the training images have different features. Additionally, if existing published quantisation tables are put into the crossover pool in the proposed GA-based system, it can improve the performance by yielding better quantisation tables. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Perceptual shape VQ of spectral envelope for efficient representation of LPC residual

    Publication Year: 2004 , Page(s): 434 - 442
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (405 KB)  

    The authors present an effective spectral envelope (SE) quantisation scheme for parametric speech coders, based on human hearing properties. The variable-dimension SE uniformly sampled vector in frequency is first converted into a fixed, but small, number of nonlinearly spaced frequency bands on the Bark scale. The minimum Bark spectral distortion (BSD) criterion is applied to enable the hearing-based SE vector quantisation (HSEVQ) scheme to quantise the SE vector, achieving a slightly better perceptual quality than the traditional method. A simplified HSEVQ (SSEVQ) scheme is developed by removing some of insensitive functions from the HSEVQ to reduce the complexity of the computation. Simulations reveal that the SSEVQ method reduces the amount of computation of the traditional SE vector quantisation scheme by a factor of nine, while retaining the quality of the reconstructed speech signal. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Generalised candidate scheme for the stochastic codebook search of scalable CELP coders

    Publication Year: 2004 , Page(s): 443 - 452
    Cited by:  Papers (3)  |  Patents (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (397 KB)  

    MPEG-4 CELP speech coders configured with multiple bit rates and coding layers provide SNR scalability, and hence may be operated effectively in variable bandwidth environments. A generalised candidate (GC) scheme is proposed to reduce the computational complexity of the stochastic codebook search of CELP coders. The experimental results demonstrate that the proposed GC scheme, incorporated with the verification model (VM) of the MPEG-4 CELP coder, enables a reduction of over 50% of the computational load without suffering any subjective quality degradations. The proposed GC scheme is not only suitable for the base layer and enhancement layers, but also facilitates computational scalability, and is therefore suitable for different working platforms and integrated media source services. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Implementation of uniform and simultaneous ART for 3-D reconstruction in an X-ray imaging system

    Publication Year: 2004 , Page(s): 360 - 368
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (986 KB)  

    The authors propose a 3-D volume reconstruction method using X-ray images with a calibration method to implement it in an X-ray imaging system. Previously the authors have proposed an advanced 3-D reconstruction algorithm based on an algebraic reconstruction technique (ART), called a uniform and simultaneous ART (USART). In practice, however, there are two main issues in implementing it in a realised X-ray imaging system. The first one is the huge computation time and memory required in achieving 3-D volume, which is a common limitation in most ART methods. The second issue is the system calibration for determining the geometry of the X-ray imaging conditions needed for the ART method. These two critical problems are addressed. A fast computing model of USART is proposed, where spherical voxel elements are employed in computation to reduce the computation time and memory. Then, a calibration method is proposed to identify the X-ray imaging geometry based on a cone beam projection model. For this purpose, a set of X-ray images of a reference grid pattern is used and the X-ray source positions are determined from the analysis of the image features, the centres of the grid points in the X-ray images. The validity of the proposed 3-D reconstruction method is investigated using a series of experiments. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Restricted structure optimal linear estimators

    Publication Year: 2004 , Page(s): 400 - 410
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (654 KB)  

    The restricted structure optimal deconvolution filtering, smoothing and prediction problem for multivariable, discrete-time linear signal processing problems is considered. A new class of discrete-time optimal linear estimators is introduced that directly minimises a minimum variance criterion but where the structure is prespecified to have a relatively simple form. The resulting estimator can be of much lower order than a Kalman or Wiener estimator and it minimises the estimation error variance, subject to the constraint referred to above. The numerical optimisation algorithm is simple to implement and the full-order optimal solutions are available as a by-product of the analysis. Moreover, the restricted structure solution may be used to compute both IIR and FIR estimators. A weighted H2 cost-function is minimised, where the dynamic weighting function can be chosen for robustness improvement. The signal and noise sources can be correlated and the signal channel dynamics can be included in the system model. The algorithm enables low-order optimal estimators to be computed that directly minimise the cost index. The main technical advance is in the pre-processing, which enables the expanded cost expression to be simplified considerably before the numerical solution is obtained. The optimisation provides a direct minimisation over the unknown parameters for the particular estimator structure chosen. This should provide advantages over the simple approximation of a high-order optimal estimator. The results are demonstrated in the estimation of a signal heavily contaminated by both coloured and white noise. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Convergence analysis of the adaptive lattice filter for a mixed Gaussian input sequence

    Publication Year: 2004 , Page(s): 428 - 433
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (290 KB)  

    The convergence performance of the adaptive lattice filter (ALF) using the stochastic gradient algorithm is measured by the convergence speed and estimated error variance of the PARCOR coefficient. The convergence properties of the ALF are analysed when the filter input has a Gaussian mixture distribution. First, theoretical expressions for the convergence rate and asymptotic error variance of the PARCOR coefficient are derived, and then the theoretical expressions are compared for single and mixed Gaussian input sequences. It is shown that the convergence performance of the ALF improves as the distribution of the input signal approaches a single Gaussian distribution. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Efficient tree structured motion estimation using successive elimination

    Publication Year: 2004 , Page(s): 369 - 377
    Cited by:  Papers (5)  |  Patents (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (558 KB)  

    In H.264/AVC, tree structured motion estimation enhances the coding efficiency significantly while dramatically increasing the computational complexity of block matching. In the paper, a successive elimination algorithm (SEA) is implemented in tree structured motion estimation with a simple and effective method to determine the initial motion vector, which exploits the strong correlation among the partially overlapped variable-size blocks. With identical performance to a full search algorithm, computations for block matching can be reduced to 1%-20%. Further, the SEA can be improved by incorporating two early termination conditions, then named 'Quick SEA'. Finally, a novel fast motion estimation algorithm, successive elimination diamond search (SEDS), is proposed by efficiently integrating the Quick SEA and a modified diamond search pattern. Simulation results show that the proposed Quick SEA can reduce the computational complexity of block matching by 3-5 times compared to the basic SEA. SEDS further reduces by about one-half the computations of Quick SEA. With similar rate distortion performance, 0.2%-1% block matching distortion is calculated for SEDS with corresponding speed-up factors of 100 to 500 in comparison with the full search algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Two-dimensional subband transforms: theory and applications

    Publication Year: 2004 , Page(s): 389 - 399
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (767 KB)  

    The one-dimensional subband FFT (SB-FFT) and one-dimensional SB-DCT were extended to the two-dimensional (2-D) case to obtain the 2-D SB-FFT and the 2-D SB-DCT. The two-dimensional subband transforms are based on subband decomposition of the input sequence in both dimensions. They use knowledge about the input signal to obtain an approximation to their transform by discarding the computations in bands that have little energy in both dimensions. Computational savings can be obtained from calculating only the remaining subbands. In many applications the computational speed is so important that some error in the calculated transform can be accepted. In image processing, due to the nature of most natural scenes, most of the energy content of the corresponding digitised images is concentrated predominantly in the low-low spatial frequency domain. The concentration of the energy in a localised region of the transform domain makes the approximate subband transform computation quite suitable for the calculation of the 2-D image spectra. The complexity and accuracy of both 2-D transforms are studied in detail in the paper. The approximation errors in both transforms are derived for a general case, in which any band out of M bands is to be computed. Both transforms are modified to be fully adaptive to select the band of interest to be computed. Image transform application examples are included. Savings in computational complexity of image transforms are shown. The efficiency of subband transforms of different images is indicated by computing the signal-to-noise ratio in the reconstructed images. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Chick feather pattern recognition

    Publication Year: 2004 , Page(s): 337 - 344
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (834 KB)  

    A crescent model is proposed for chick wing image processing and feather pattern recognition, thereby implementing chick sex separation by machine vision technology. The crescent shape delineates the region of interest in a wing image by an arc of large radius and an arc of small radius at two off-centred circles. Wing feathers are divergently distributed in the crescent region, manifesting as an oriented stripe pattern. Male chick feathers gradually change in length from short to long and then to short in accordance with the crescent envelope. Female chick feathers alternate the stripe lengths, following a long-short-long stripe pattern. Based on this knowledge, a chick feather pattern can be numerically characterised by a stripe length sequence and a stripe endpoint sequence. For pattern classification, the first-order differences of these two sequences are used. The mean value of the stripe endpoint difference sequence is the most efficient feature in male-female chick classification. Experimental results justified the model and feature selection strategy, and showed the feasibility of automatic chick sex separation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image reconstruction by conditional entropy maximisation for PET system

    Publication Year: 2004 , Page(s): 345 - 352
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (686 KB)  

    The authors show that the conditional entropy maximisation algorithm is a generalised version of the maximum likelihood algorithm for positron emission tomography (PET). Promising properties of the conditional entropy maximisation algorithm are as follows: an assumption is made that the entropy of the information content of the data should be maximised; it is a consistent way of selecting an image from the very many images that fit the measurement data; this approach takes care of the positivity of the reconstructed image pixels, since entropy does not exist for negative image pixel values; and inclusion of prior distribution knowledge in the reconstruction process is possible. Simulated experiments performed on a PET system have shown that the quality of the reconstructed image using the entropy maximisation method is good. A Gibbs distribution is used to incorporate prior knowledge into the reconstruction process. The mean squared error (MSE) of the reconstructed images shows a sharp new dip, confirming improved image reconstruction. The entropy maximisation method is an alternative approach to maximum likelihood (ML) and maximum a posteriori (MAP) methodologies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Phase regression approach for estimating the parameters of a noisy multifrequency signal

    Publication Year: 2004 , Page(s): 411 - 420
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (337 KB)  

    A novel approach for estimating the parameters of a multifrequency signal from discrete samples corrupted by additive noise is presented. An established mathematical model indicates that noise influence on the discrete phase and amplitude spectra is equivalent to additive phase and amplitude noise, respectively. On this basis, a simple algorithm is proposed to estimate the frequency and phase of each sinusoid component by linear regression on the phase spectra of segmented signal blocks, while an amplitude estimator is directly derived from the spectrum of the window function. The circular nature of the phase spectrum is thoroughly explored. Also, an algorithmic scheme is presented. The derived variances of the estimators show that for a noisy signal this approach provides superior accuracy over the traditional approaches. Simulations and engineering application confirm the validity of the presented method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Compressing image-based relighting data using eigenanalysis and wavelets

    Publication Year: 2004 , Page(s): 378 - 388
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (832 KB)  

    In image-based relighting (IBL) a tremendous number of reference images are needed to synthesise a high-quality novel image. This collection of reference images is referred as an IBL data set. An effective compression method for IBL data makes the IBL technique more practical. Within an IBL data set, there is a strong correlation among different reference images. In conventional eigen-based image compression methods, the principal component analysis (PCA) process is used for exploiting the correlation within a single image. Such an approach is not suitable for handling IBL data. The authors present an eigenimage-based method for compressing IBL data. The method exploits the correlation among reference images. Since there is a huge number of images and pixel values, the cascade recursive least square (CRLS) network based PCA is used to extract eigenimages. Afterwards, the wavelet approach is used for compressing those eigenimages. Simulation results demonstrate that this approach is much superior to that of compressing each reference image with JPEG and JPEG2000. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.