Nonlinear Interpolative Vector Quantization Research Articles

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.

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.

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.

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>