The conventional shape from focus (SFF) methods have inaccuracies because of piecewise constant approximation of the focused image surface (FIS). We propose a more accurate scheme for SFF based on representation of three-dimensional FIS in terms of neural network weights. The neural networks are trained to learn the shape of the FIS that maximizes the focus measure.

We present a novel approach to developing Machine Learning (ML) based decoding models for extracting a watermark in the presence of attacks. Statistical characterization of the components of various frequency bands is exploited to allow blind extraction of the watermark. Experimental results show that the proposed ML based decoding scheme can adapt to suit the watermark application by learning the alterations in the feature space incurred by the attack employed.

In this paper, we present two fast motion estimation techniques with adaptive variable search range using spatial and temporal correlation of moving pictures respectively. The first technique uses a frame difference between two adjacent frames which is used as a criterion for deciding search window size. The second one uses deviation between the past and the predicted current frame motion vectors which is also used as a criterion for deciding search window size. Simulation results show that these methods reduce the number of checking points while keeping almost the same image quality as that of full search method.

A classification method is presented for differentiating honeycombed High Resolution Computed Tomographic (HRCT) images from normal HRCT images. For successful classification of honeycombed HRCT images, a complete set of methods and algorithms is described from segmentation to extraction to feature selection to classification. Wavelet energy is selected as a feature for classification using K-means clustering. Test data of 20 patients are used to validate the method.

To reduce an amount of computation of full search algorithm for fast motion estimation, we propose a new and fast matching algorithm without any degradation of predicted images. The computational reduction without any degradation comes from adaptive matching scan algorithm according to the image complexity of the reference block in current frame. Experimentally, we significantly reduce the computational load compared with conventional full search algorithm.

We propose a new method for Depth from Defocus (DFD) using wavelet transform. Most of the existing DFD methods use inverse filtering in a transform domain to determine the measure of defocus. These methods suffer from inaccuracies in finding the frequency domain representation due to windowing and border effects. The proposed method uses wavelets that allow performing both the local analysis and windowing with variable-sized regions for images with varying textural properties. Experimental results show that the proposed method gives more accurate depth maps than the previous methods.

This paper presents a novel wavelet compression technique to increase compression of images. Based on zerotree entropy coding method, this technique initially uses only two symbols (significant and zerotree) to compress image data for each level. Additionally, sign bit is used for newly significant coefficients to indicate them being positive or negative. Contrary to isolated zero symbols used in conventional zerotree algorithms, the proposed algorithm changes them to significant coefficients and saves its location, they are then treated just like other significant coefficients. This is done to decrease number of symbols and hence, decrease number of bits to represent the symbols used. In the end, algorithm indicates isolated zero coordinates that are used to change the value back to original during reconstruction. Noticeably high compression ratio is achieved for most of the images, without changing image quality.

Wavelet based image compression is getting popular due to its promising compaction properties at low bitrate. Zerotree wavelet image coding scheme efficiently exploits multi-level redundancy present in transformed data to minimize coding bits. In this paper, a new technique is proposed to achieve high compression by adding new zerotree and significant symbols to original EZW coder. Contrary to four symbols present in basic EZW scheme, the modified algorithm uses eight symbols to generate fewer bits for a given data. Subordinate pass of EZW is eliminated and replaced with fixed residual value transmission for easy implementation. This modification simplifies the coding technique as well and speeds up the process, retaining the property of embeddedness.

Chain codes were developed for storing contour information for shape matching. The traditional chain codes are highly susceptible to small perturbations in the contours of the objects. Therefore, traditional chain codes could not be used for image retrieval based on the shape boundaries from the large databases. In this paper, a histogram based chain codes are proposed which could be used for image retrieval. The proposed chain codes are invariant to translation, rotation and scaling transformations, and have high immunity to noise and small perturbations.

A new approach to 3-D profilometry for the white light interferometric (WLI) is presented. The proposed method is the extended depth from focus (EDFF) that determine the zero optical path difference (OPD) from the quantity of fringe contrast degradation of white light interferometer. In the method, the variance of the mismatch function and the modified local variance function are used as the focus measures. The method has a theoretically unlimited range and can profile with subpixel accuracy both optically rough and smooth surfaces without changing algorithm.

This paper presents efficient file management of a hard disk drive embedded digital satellite receiver. The digital broadcasting technology enables multimedia access via broadcasting systems. The amount of digital data to be processed is increased remarkably as compared to the previous analog broadcasting environments. The efficient digital data storage and management technology are discussed in this paper to cope with these changes. The DSR uses a new file system that is designed by considering disk cluster sizes and limited memories in the system, which is more appropriate than that of general Personal Computers. The proposed system enables us to watch broadcasting and to manage multimedia data efficiently.

A new UEP technique for image transmission using trellis code based on Hamming distance criterion has been proposed. The simulation results comparing the image quality and bit-rate for UEP and EEP have been provided. The results show that UEP performs better than EEP in terms of bit-rate without any significant depreciation in image quality.

The rapid growth of multimedia applications has increased interest in the compression of video data. This paper presents a new method for improving the compression ratio of video data, which can be easily used in a multilayer environment for error resilience applications as well. Data of four luminance blocks in a macroblock are processed and arranged in such a way that important macroblock data is compressed in one block(A), while the rest of the three remaining data blocks(H,V,D) are given difference values in the horizontal, vertical and diagonal directions. This results in a reduced bitstream size because of the low-valued data present in the three blocks(H,V,D), giving better compression at low bitrates. In an error resilient environment, the important data block in a macroblock is transmitted in a secure channel while the remaining three blocks with difference data are sent via a lossy channel. If error occurs in the lossy channel, picture can still be reconstructed with reasonably good quality using only the block data that is transmitted in the secure channel.

The artifacts of low-bit rate quantization in images cannot be removed satisfactorily by known methods. We propose decomposition of images as HSI and LSI (higher- and lower- significance images), followed by subsampling and reconstruction methods for LSI. Experiments show significant improvement in image quality, as compared to other methods.

An advanced center biased search algorithm for block motion estimation is proposed in this letter. It adopts an innovative center biased search strategy to get correct motion vector. The computational complexity is reduced by strict application of the unimodal error surface assumption and half stop technique. Experimental results show that proposed algorithm has improved performance as compared to the conventional block matching algorithms.

A new method for computing precise estimates of the motion vectors of moving objects in a sequence of images is proposed. The proposed method is based on dynamic programming matching applied along chain-coded binary contours of images. This significantly reduces the computational complexity of the correspondence matching applied to the 2-D optimization problem. Computer simulation and experimental results demonstrate a good performance of the method in terms of dynamic motion analysis.

A novel effective method for detection and removal impulse noise in highly corrupted color images is proposed. This detection-estimation method consists of two steps. Outliers are first detected using spatial relations between the color components. Then the detected noise pixels are replaced with the output of the vector median filter over a local spatially connected area excluding the outliers. Simulation results in a test color image show a superior performance of the proposed filtering algorithm comparing to the conventional vector median filter. The comparisons are made using a mean square error and a mean absolute error criteria.