A 2-D orientation-adaptive prediction filter in lifting structures for image coding

Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate ±45° in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required. © 2006 IEEE

Lossless image compression using an edge adapted lifting predictor

We present a novel and computationally simple prediction stage in a Daubechies 5/3 - like lifting structure for lossless image compression. In the 5/3 wavelet, the prediction filter predicts the value of an odd-indexed polyphase component as the mean of its immediate neighbors belonging to the even-indexed polyphase components. The new edge adaptive predictor, however, predicts according to a local gradient direction estimator of the image. As a result, the prediction domain is allowed to flip + or - 45 degrees with respect to the horizontal or vertical axes in regions with diagonal gradient. We have obtained good compression results with conventional lossless wavelet coders. © 2005 IEEE

Polyphase adaptive filter banks for fingerprint image compression

A perfect reconstruction polyphase filter bank structure is presented in which the filters adapt to the changing input conditions. The use of such a filter bank leads to higher compression results for images containing sharp edges such as fingerprint images

Coding of fingerprint images using binary subband decomposition and vector quantization

In this paper, compression of binary digital fingerprint images is considered. High compression ratios for fingerprint images is essential for handling huge amount of images in databases. In our method, the fingerprint image is first processed by a binary nonlinear subband decomposition filter bank and the resulting subimages are coded using vector quantizers designed for quantizing binary images. It is observed that the discriminating properties of the fingerprint, images are preserved at very low bit rates. Simulation results are presented

Motion-compensated prediction based algorithm for medical image sequence compression

A method for irreversible compression of medical image sequences is described. The method relies on discrete cosine transform and motion-compensated prediction to reduce intra- and inter-frame redundancies in medical image sequences. Simulation examples are presented. © 1995

Frequency band characteristics of tree-structured filter banks

A sub-band decomposition filter bank can be recursively used in a tree structure to divide the frequency domain into various subfrequency bands. The frequency bands of the sub-band signals have a counter intuitive order in such a decomposition. The authors show that the relationship between the frequency content and the index of a sub-band signal can be expressed by an extension of the Gray code

Subband domain coding of binary textual images for document archiving

In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented

Nonlinear subband decomposition structures in GF-(N) arithmetic

In this paper, perfect reconstruction filter bank structures for GF-(N) fields are developed. The new filter banks are based on the nonlinear subband decomposition and they are especially useful to process binary images such as document and fingerprint images. © 1998 Elsevier Science B.V. All rights reserved

Image Coding With Wavelet Representations, Edge Information And Visual Masking

The wavelet transform provides a multiresolution representation of images. Edges, which are visually important, produce large coefficients across several scales in the wavelet transform domain. By tracking and predicting these edge coefficients across scales in the wavelet transform domain, we can greatly improve the compressed image quality with little degradation in compression ratio. This paper proposes a novel model-based edge tracking and prediction in wavelet domain. It separates textures from edges and codes them differently. Edges are coded via an edge tracking and prediction, while textures are coded with either ordinary wavelet based image coding techniques or a "wavelet-like" filter bank which is similar to the tuning channels in human vision system. The coding noise is then coded with a noise modelling. Visual masking models are also used to ensure the compressed image has little or almost no perceptual distortion. 1. INTRODUCTION Low bitrate image compression with little..

Region covariance descriptors calculated over the salient points for target tracking [Hedef i̇zleme i̇çi̇n önemli̇ noktalar üzeri̇nden hesaplanan bölgesel ortak deǧi̇ş i̇nti̇ beti̇mleyi̇ci̇leri̇]

Features extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasi's minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure. © 2012 IEEE