Information hiding or data hiding, also known as watermarking, has become a part and parcel of covert communication and copyright protection. Maximizing watermark payload is a major challenge for watermark researchers. To overcome this issue, we have proposed a new color image watermarking technique, using residue number system (RNS). RNS refers to a large integer using a set of smaller integers which relies on the Chinese remainder theorem of modular arithmetic for its operation. The proposed method takes pixel values from three watermark images and embeds them into the main cover image. Experimental results presented in this paper shows that the watermark can be successfully embedded and extracted from an image, without distorting the original image using the proposed technique. The high peak signal to noise ratio (PSNR) and payload values claims the robustness of the proposed method.

In this paper, we present an adaptive deblocking filter to improve the video quality for high efficiency video coding (HEVC) scheme. The HEVC standard is a hybrid coding scheme using block-based prediction and transform encoding/decoding. At the decoding step, the boundary of any two adjacent blocks causes visual discontinuities called blocking artifacts that can be removed using deblocking filter. Conventional approaches, including the HEVC standard, tend to remove those artifacts using two offset parameters that are defaulted to 0. However, such a choice is not necessarily suitable to encode/decode all video sequences. The proposed approach reduces an exhaustive search among a set of candidate offsets to eventually select the best offsets adaptively (i.e., for each frame) according to some characteristics of the data sequences. Improvements are shown using the proposed approach in terms of rate-distortion (RD) performance as opposed to the HEVC standard without changing the compression ratio and with negligible change in the encoding/decoding time.

Feature extraction is an important step in image classification. It allows to represent the content of images as perfectly as possible. However, in this paper, we present a comparison protocol of several feature extraction techniques under different classifiers. We evaluate the performance of feature extraction techniques in the context of image classification and we use both binary and multiclass classifications. The analyses of performance are conducted in term of: classification accuracy rate, recall, precision, f-measure and other evaluation measures. The aim of this research is to show the relevant feature extraction technique that improves the classification accuracy rate and provides the most implicit classification data. We analyze the models obtained by each feature extraction method under each classifier.

Object Tracking is becoming increasingly important in areas of computer vision, surveillance, image processing and artificial intelligence. The advent of high powered computers and the increasing need of video analysis has generated a great deal of interest in object tracking algorithms and its applications. This said it becomes even more important to evaluate these algorithms to quantify their performance. In this paper, we have implemented three algorithms namely Alpha Beta filter, Kalman filter and Meanshift to track an object in a video sequence and compared their tracking performance based on various parameters in normal and noisy conditions. The proposed parameters employed are error plots in position and velocity of the object, Root mean square error, object tracking error, tracking rate and time taken to track the object. The goal is to illustrate practically the performance of each algorithm under such conditions quantitatively and identify the algorithm that performs the best.

The overlapping factor has a vital role in determining the optimum bandwidth utilization and the desired side band suppression. In this paper, we review one of the filter bank multicarrier technique-staggered multi tone modulation, discuss its efficient implementation in polyphase form and compare between several performance parameters of the same. We also discuss the role of overlapping factor in staggered multi tone modulation systems. We describe a low complexity procedure for generation of the required overlapping factors based on frequency sampling method. Our simulations describe the effectiveness of the optimum value selection for overlapping factor in a staggered multi tone modulation system. The proposed system is theoretically found to be compatible with the Long Term Evolution standards. The performance evaluation of the proposed system in ideal conditions emphasizes about the reduction in spectrum leakage in sidebands of the staggered multi tone modulation system, along with a little increase in system complexity.

Sparse representation based super resolution deals with the problem of reconstructing a high resolution image from one or several of its low resolution counterparts. In this case the low resolution image is modelled as the down-sampled version of its high resolution counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e. the low resolution image is directly down sampled from its high resolution counterpart without blurring and the super-resolution problem becomes an image interpolation problem. In such cases, the conventional sparse representation models become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, the given image patch can be modelled as the linear combination of nonlocal similar neighbours. In this paper image nonlocal self-similarity for image interpolation is introduced. More specifically, wavelet based a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in sparse representation model. Our experimental results on benchmark test images clearly demonstrate that the proposed wavelet-NARM based image interpolation method outperforms the reconstruction of edge structures and suppression of jaggy/ringing artefacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as structural similarity index and structural content. The proposed method is applied on bio medical images to emphasis on diagnostic information.

Patient information is embedded inside the medical images for the storage or transmission, or healthcare applications. In medical image processing, various types of noises corrupt the image quality. There is a need of measure specific noise for a particular image is required for the evaluation of robustness for embedding techniques used for hiding patient information in medical images. It is very important to obtain precise images to facilitate accurate analysis and estimation of noise in embedded medical image. The current work is focused towards studying the effect of specific noise which affect particular medical image. The strength of the medical image is tested by introducing several attacks to the embedded medical images. The statistical quantity measures like peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and normalized root mean square error (NRMSE) are employed to measure the quality of the output medical image.

Future high resolution instrument planned by ISRO for space remote sensing will lead to higher data rates because of increase in resolution and dynamic range. Hence, image compression implementation becomes mandatory. Presently designed compression technique does not take account of imaging system noise like photon noise etc. This ignorance affects compression system performance. As a solution, this paper proposes MLG (Multi Linear Gain) operation prior to main compression system. With digital MLG operation, captured image can be optimally adjusted to systems noise. Proposed MLG operation improves compression ratio. Simulation results show 15-30% improvement in lossless compression ratio. However this improvement depends on MLG gains and corner points which can be driven by system SNR plot. MLG operation also helps in improving SNR at lower radiance input, when lossy JPEG2000 compression is used as main compression. Up to 1-6 dB SNR improvement is observed in simulations. Proposed MLG implementation is of very low complexity and planned to be used in future missions.