A novel modular coding paradigm is investigated using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.

A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multiresolution features and by the introduction of a higher level neural network on the top of the individual networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data-decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of real FLIR images.

In this paper, we present a modular neural network vector predictor that improves the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes, based on its directional variances. One expert predictor is optimized for stationary blocks, and each of the other four expert predictors are optimized to predict horizontal, vertical, 45 degrees , and 135 degrees diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is transmitted to the receiver about the selected predictor or the integration of the predictors. Experimental results show that the proposed scheme gives an improvement of 1.7 dB over a single multilayer perceptron (MLP) predictor. Furthermore, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over a single MLP predictor. The perceptual quality of the predicted images is also significantly improved.

This paper presents a predictive residual vector quantization (PRVQ) scheme using a modular neural network vector predictor. The proposed PRVQ scheme takes the advantage of the high prediction gain and the improved edge fidelity of a modular neural network vector predictor in order to implement a high performance vector quantization (VQ) scheme with low search complexity and a high perceptual quality. Simulation results show that the proposed PRVQ with modular vector predictor outperforms the equivalent PRVQ with general vector predictor (operating at the same bit rate) by more than 1 dB. Furthermore, the perceptual quality of the reconstructed image is also improved.

This paper investigates a novel modular image coding paradigm using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks, where each expert network is optimized for predicting a particular class of data, and an integrating unit that mixes the outputs of the expert networks in order to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (MPRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same PSNR, the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.

Composite classifiers consisting of a number of component classifiers have been designed and evaluated on the problem of automatic target recognition (ATR) using a large set of real forward-looking infrared (FLIR) imagery. Two existing classifiers are used as the building blocks for our composite classifiers. The performance of the proposed composite classifiers are compared based on their classification ability and computational complexity. It is demonstrated that the composite classifier based on a cascade architecture greatly reduces the computational complexity with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.

The performance of a classical linear vector predictor is limited by its ability to exploit only the linear correlation between the blocks. However, a nonlinear predictor exploits the higher order correlations among the neighboring blocks, and can predict edge blocks with increased accuracy. We have investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron (MLP), the functional link (FL) network, and the radial basis function (RBF) network. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor.

In this paper, we present a segmentation based wavelet coding scheme, in which an image is segmented into two regions: stationary areas (background) and the areas containing edge information (foreground). These regions are then encoded independently using two dedicated encoders that are optimized for each region. A 2-D edge operator is used for segmenting the image. We use the embedded zerotree wavelet (EZW) algorithm for encoding the background due to its good performance on stationary areas. The foreground area is, however, encoded using a predictive residual vector quantizer (PRVQ). Experimental results show that the proposed technique improves the quality of the reconstructed images, both numerically (in terms of mean square error) and perceptually when compared to EZW at the same bit rate.

In this paper, we present a modular neural network vector predictor in order to improve the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes based on its directional variances. One expert is optimized for stationary blocks, and each of the other four experts are optimized to predict horizontal, vertical, 45/spl deg/, and 135/spl deg/ diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is required to transmit to the receiver about the predictor selection. Experimental results show that the proposed scheme gives an improvement of 1 to 1.5 dB better than a single multilayer perceptron (MLP) predictor. However, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over the single MLP predictor. The perceptual quality of the predicted images is also significantly improved.

A vector predictor is an integral part of a predictive vector quantization coding scheme. The conventional techniques for designing a nonlinear predictor are extremely complex and suboptimal due to the absence of a suitable model for the source data. We investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron, the functional link network and the radial basis function network. We also evaluated and compared the performance of these neural network predictors with that of a linear vector predictor. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor. However, the performance of a neural network predictor is comparable to that of a linear predictor for predicting the stationary and shade blocks.

A major problem with a VQ based image compression scheme is its codebook search complexity. Recently, a new VQ scheme called predictive residual vector quantizer (PRVQ) was proposed by Rizvi and Nasrabadi (see Proc. IEEE Int. Conf. Image Processing (Austin), vol.1, p.608-12, Nov. 13-16, 1994) which has a performance very close to that of the predictive vector quantizer (PVQ) with very low search complexity. This paper presents a new variable-rate VQ scheme called entropy-constrained PRVQ (EC-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed EC-PRVQ is found to give a good rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG). The robustness of EC-PRVQ is demonstrated by encoding several test images taken from outside the training data.

A vector predictor is an integral part of the predictive vector quantization (PVQ) scheme. The performance of a predictor deteriorates as the vector dimension (block size) is increased. This makes it necessary to investigate new design techniques in order to design a vector predictor which gives better performance when compared to a conventional vector predictor. This paper investigates several neural network configurations which can be employed in order to design a vector predictor. The following architectures are investigated: (a) multilayer perceptron, (b) functional link network, and (c) radial basis function network. The performance of the above mentioned neural network vector predictors is evaluated and compared with that of a linear vector predictor.