Interpolative Vector Quantization Research Articles

Computation of the complexity of vector quantizers by affine modeling

Several methods for evaluation of the complexity of data compression systems and for including complexity measures in the traditional rate-distortion analysis have been published in recent works. In this work, we indicate that the relationship between rate-distortion performance and complexity for some practical coding schemes—entropy-constrained vector quantization (ECVQ) and interpolative vector quantization (IVQ)—can be represented by affine models. For the same rate-distortion performance, the complexity of an interpolative vector quantizer is known to be significantly smaller than the complexity of a full-search entropy constrained vector quantizer, and this complexity difference is a suitable illustration for the rate-distortion–complexity framework. We use high-resolution theory arguments to derive the affine models for ECVQ and IVQ. The proposed affine complexity modeling successfully predicts the cost of vector quantizers designed from data sets that were not used to generate the models.

Image interpolation using interpolative classified vector quantization

According to advances in digital imaging technology, interest in high-resolution (HR) images has been increased. Various methods that convert low-resolution (LR) images to HR ones have been presented. In this paper, to reduce the computational load we propose a vector quantization (VQ) based algorithm that reconstructs an interpolation image by adding to an initially interpolated image high-frequency components predicted from training with a number of example image sets. The proposed interpolative classified VQ (ICVQ) algorithm combines interpolative VQ with classified VQ. With a number of (LR and HR) example image sets, we construct two types of (LR and HR) codebooks. Comparative experiments with three conventional image interpolation algorithms show that the proposed interpolation algorithms using ICVQ effectively preserve edges to which the human visual system is sensitive. The proposed algorithm can be applicable to various image- and video-based applications such as digital camera and digital television.

An invisible hybrid color image system using spread vector quantization neural networks with penalized FCM

In this paper, an invisible hybrid color image hiding scheme based on spread vector quantization (VQ) neural network with penalized fuzzy c-means (PFCM) clustering technology (named SPFNN) is proposed. The goal is to offer safe exchange of a color stego-image in the internet. In the proposed scheme, the secret color image is first compressed by a spread-unsupervised neural network with PFCM based on interpolative VQ (IVQ), then the block cipher Data Encryption Standard (DES) and the Rivest, Shamir and Adleman (RSA) algorithms are hired to provide the mechanism of a hybrid cryptosystem for secure communication and convenient environment in the internet. In the SPFNN, the penalized fuzzy clustering technology is embedded in a two-dimensional Hopfield neural network in order to generate optimal solutions for IVQ. Then we encrypted color IVQ indices and sorted the codebooks of secret color image information and embedded them into the frequency domain of the cover color image by the Hadamard transform (HT). Our proposed method has two benefits comparing with other data hiding techniques. One is the high security and convenience offered by the hybrid DES and RSA cryptosystems to exchange color image data in the internet. The other benefit is that excellent results can be obtained using our proposed color image compression scheme SPFNN method.

Wavelet-based image compression using classified interpolative vector quantization

A new method of nonlinear interpolative vector quantization for wavelet-transform image coding is proposed. Training and quantization are carried out by classifying the coefficient vectors in the wavelet- transform domain according to their directional activity energy. A product codebook is used during encoding and decoding to allocate bit assignments among these classes adaptively. Compared to the conventional interpolative vector quantization technique, the new method gives superior compression performance and also reduces the complexity of codebook training and quantization. The PSNR improvement is about 1 dB at the compression ratio of 50.

Training of a neural network for image superresolution based on a nonlinear interpolative vector quantizer

Superresolution is the process of extending the spectrum of a diffraction-limited image beyond the optical passband. We consider the neural-network approach to accomplish superresolution and present results on simulated gray-scale images degraded by diffraction blur and additive noise. Images are assumed to be sampled at the Nyquist rate, which requires spatial interpolation for avoiding aliasing, in addition to frequency-domain extrapolation. A novel, to our knowledge, use of vector quantization for the generation of training data sets is also presented. This is accomplished by training of a nonlinear vector quantizer, whose codebooks are subsequently used in the supervised training of the neural network with backpropagation.

A generalized interpolative vector quantization method for jointly optimal quantization, interpolation, and binarization of text images

This paper presents an approach for the effective combination of interpolation with binarization of gray level text images to reconstruct a high resolution binary image from a lower resolution gray level one. We study two nonlinear interpolative techniques for text image interpolation. These nonlinear interpolation methods map quantized low dimensional 2 x 2 image blocks to higher dimensional 4 x 4 (possibly binary) blocks using a table lookup operation. The first method performs interpolation of text images using context-based, nonlinear, interpolative, vector quantization (NLIVQ). This system has a simple training procedure and has performance (for gray-level high resolution images) that is comparable to our more sophisticated generalized interpolative VQ (GIVQ) approach, which is the second method. In it, we jointly optimize the quantizer and interpolator to find matched codebooks for the low and high resolution images. Then, to obtain the binary codebook that incorporates binarization with interpolation, we introduce a binary constrained optimization method using GIVQ. In order to incorporate the nearest neighbor constraint on the quantizer while minimizing the distortion in the interpolated image, a deterministic-annealing-based optimization technique is applied. With a few interpolation examples, we demonstrate the superior performance of this method over the NLIVQ method (especially for binary outputs) and other standard techniques e.g., bilinear interpolation and pixel replication.

Lapped nonlinear interpolative vector quantization and image super-resolution

This correspondence presents an improved version of an algorithm designed to perform image restoration via nonlinear interpolative vector quantization (NLIVQ). The improvement results from using lapped blocks during the decoding process. The algorithm is trained on original and diffraction-limited image pairs. The discrete cosine transform is again used in the codebook design process to control complexity. Simulation results are presented which demonstrate improvements over the nonlapped algorithm in both observed image quality and peak signal-to-noise ratio. In addition, the nonlinearity of the algorithm is shown to produce super-resolution in the restored images.

A vector quantizer for image restoration

This paper presents a novel technique for image restoration based on nonlinear interpolative vector quantization (NLIVQ). The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. It is trained on image pairs consisting of an original image and its diffraction-limited counterpart. The discrete cosine transform is used in the codebook design process to control complexity. Simulation results are presented that demonstrate improvements in visual quality and peak signal-to-noise ratio of the restored images.

A novel interpolative vector quantization scheme

Abstract A novel adaptive interpolative vector quantization scheme is developed and presented in this paper. Instead of constructing an approximation of the original image with the traditional fix-rate subsampling and interpolation process, a new dominant point detection algorithm is employed to build a non-uniformly spaced decimation lattice. In effect, better approximation and lower bit-rate are achieved by extracting only those sampling points which have significant effects on the aliasing error. The difference between the original and the approximated image is encoded with vector quantization . Experimental results show that the decoded images are found to exhibit coding fidelity of over 34 dB with improved visual quality over existing vector quantization techniques. A comparison of the proposed scheme with other existing methods of similar complexity has also been presented.

Image scalability using wavelet vector quantization

Spatial scalability is a feature referring to image representation in different sizes and is particularly useful in image browsing, as well as in progressive image transmission applications. We propose a new technique for scaling images based on combined wavelet transform and vector quantization (VQ). The proposed technique exploits the spatial scalability feature of wavelet transform and the efficient compression performance of vector quantization. Although VQ is a powerful technique for low bit rate image compression, the label stream VQ is not scalable. Hence, we first propose a scalable VQ algorithm (SVQ) where a smaller-size, coarse quality image can be obtained by decoding a portion of the label bitstream. This image can be further enhanced in size by progressively decoding the remaining bits of the label bitstream. We note that to ensure partial decodability of VQ labels, a multiresolution codebook structure is required. We then apply the SVQ technique in the wavelet domain (WSVQ) where the input image is wavelet-decomposed into three levels and the resulting coefficients are organized into vectors to exploit the intra- and interband correlations. The interband correlations are exploited by employing a nonlinear interpolative vector quantization. Simulation results confirm the superior subjective performance of the proposed technique at a significant reduced computational complexity.

Wavelet-based image coding using nonlinear interpolative vector quantization

We propose a reduced complexity wavelet-based image coding technique. Here, 64-D (for three stages of decomposition) vectors are formed by combining appropriate coefficients from the wavelet subimages, 16-D feature vectors are then extracted from the 64-D vectors on which vector quantization (VQ) is performed. At the decoder, 64-D vectors are reconstructed using a nonlinear interpolative technique. The proposed technique has a reduced complexity and has the potential to provide a superior coding performance when the codebook is generated using the training vectors drawn from similar images.

Brick-wall structured segmentation for interpolative vector quantization of images

Abstract A new variable block size image segmentation scheme based on the modified split-and-merge (MSM) algorithm is devised in this paper. It is shown that the position of the sampling points obtained from the MSM algorithm are located at the edge boundaries and the distance between two consecutive sampling points indicates the local activity between them. An image is partitioned using the properties of the selected sampling points. The proposed method is applied for coding of monochrome image using interpolative vector quantization (IVQ) technique. Simulation results show that the proposed method can achieve 0.8 dB PSNR improvement at a bit rate lower than the fixed block size scheme. Besides, perfect reconstruction of edges is resulted and hence excellent reconstructed image visual quality is obtained.

An improved interpolative vector quantization scheme using non-recursive adaptive decimation

In the proposed method, an approximation of the original image is constructed using a novel adaptive decimation algorithm, and the residual image is encoded using Vector Quantization. A relatively higher coding fidelity and lower bit-rate are attained.

Localization of dominant points for image coding

In this letter, a modified dominant point detection algorithm is developed and applied for Interpolative Vector Quantization (IVQ) of images. The algorithm employs an optimal discontinuity detector to locate first order discontinuities which efficiently characterizes a scan line. Using the detected first order discontinuities as the initial guess, the final set of interpolating points is obtained using a Modified Split-&-Merge algorithm. Experimental results show that, under the same bit-rate, an average of 0.5 dB improvement is achieved by our proposed method when compared with the results obtained by the traditional IVQ in terms of Peak Signal-to-Noise Ratio (PSNR).

A high quality image compression technique for low cost multimedia applications

A novel image compression technique suitable for low cost multimedia applications is reported. The method is a class of interpolative vector quantization which, in the past, is implemented using fix-rate subsampling and interpolating processes. In the proposed technique, a new adaptive sampling point detection algorithm is employed to construct non-uniformly spaced decimation lattice. In effect, only sampling points which have significant effects on the aliasing error are extracted; hence providing superior coding fidelity at relatively lower bit-rate.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Optimal nonlinear interpolative vector quantization

A process by which a reduced-dimensionality feature vector can be extracted from a high-dimensionality signal vector and then vector quantized with lower complexity than direct quantization of the signal vector is discussed. In this procedure, a receiver must estimate, or interpolate, the signal vector from the quantized features. The task of recovering a high-dimensional signal vector from a reduced-dimensionality feature vector can be viewed as a generalized form of interpolation or prediction. A way in which optimal nonlinear interpolation can be achieved with negligible complexity, eliminating the need for ad hoc linear or nonlinear interpolation techniques, is presented. The range of applicability of nonlinear interpolative vector quantization is illustrated with examples in which optimal nonlinear estimation from quantized data is needed for efficient signal compression.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Interpolative vector quantization of color images

Interpolative vector quantization has been devised to alleviate the visible block structure of coded images plus the sensitive codebook problems produced by a simple vector quantizer. In addition, the problem of selecting color components for color picture vector quantization is discussed. Computer simulations demonstrate the success of this coding technique for color image compression at approximately 0.3 b/pel. Some background information on vector quantization is provided. >