Ayush Kumar

A low complex context adaptive image interpolation algorithm for real-time applications

Recently a lot of interpolation algorithms are proposed, but these interpolation algorithms are highly computationally expensive. Hence these algorithms cannot be implemented and used in real time applications. In view of real time applications we have proposed a computationally simple interpolation algorithm. In our proposed algorithm the unknown pixels are categorized into various bins depending upon the characteristic of the neighboring pixels (activity level) and for each bin fixed prediction parameters are used for prediction. We have presented different set of fixed predictors for both smooth type and edgy type of images. We have also proposed a modified algorithm in which selection of prediction parameter is done on block by block basis instead of image basis. Our proposed algorithm gives much better qualitative and quantitative performance as compared to other computationally simple interpolation algorithms.

An efficient edge preserving image interpolation algorithm

Quality degradation and computational complexity are the major challenges for image interpolation algorithms. Advanced interpolation techniques achieve to preserve fine image details but typically suffer from lower computational efficiency, while simpler interpolation techniques lead to lower quality images. In this paper, we propose an edge preserving technique based on inverse gradient weights as well as pixel locations for interpolation. Experimental results confirm that the proposed algorithm exhibits better image quality compared to conventional algorithms. At the same time, our approach is shown to be faster than several advanced edge preserving interpolation algorithms.

An adaptive distance-based edge preserving interpolation algorithm for natural images

In this paper, we propose a new image interpolation method using adaptive weights based on inverse gradients and distances from the pixels used in prediction. Since the weights are based on different spatial locations of the pixels, this allows preservation of important edge information and hence to prevent extensive blurring across edges in the upsampling process. Experimental results on a large test image set show that the proposed algorithm gives better performance compared to conventional algorithms.

Payload minion stenographic technique for color images

Incredible evolution of Internet, Digital devices and communication channels lead to an enormous increase in data leading to demand of Data security systems. Image stenography is the technique to hide data inside an image. This paper proposes a message dependent image stenography technique for concealing information into a cover image. Arnold transform is used with 24-bit key insertion before Haar Wavelet Transform based compression of message signal ensuring the security of the message from the untrusted third party. Then based on the statistical features and Payload efficiency decided by the user, the technique finds the adequate coefficients from cover image maintaining the high visual quality for the image. Experimental results show that the technique works better for payload less than 50%as the PSNR for 50 images were found to be 42.75dB. Higher the payload lowers the quality of staego image obtained.

An efficient 2-D jacobian iteration modeling for image interpolation

This paper proposes a new interpolation approach for obtaining high resolution (HR) images from its low resolution (LR) images. We are using the Least Squared based block by block prediction scheme to estimate the predictors using Jacobian iteration method. In spite of Jacobians Iterative property of convergence for diagonally dominant matrices only, our proposed method uses this property effectively for all types of matrices, and found a set of prediction coefficients using a small number of iterative steps. Due to its lesser computational cost it can be used in real time applications too. Use of iterative methods like Jacobi gives an advantage of its application over images which gives singular matrices during operation. Experimental results indicates that the proposed algorithm gives better quantitative performance as compared to other conventional interpolation techniques.

A switching based adaptive image interpolation algorithm

In this paper, we propose a new adaptive image interpolation algorithm for enhancement of natural images. The proposed method uses different algorithms namely SAI, SPIA and Context-Based Image Interpolation Algorithm (CBIA) techniques, for both edgy and smooth type of images. The detailed part of smooth type image is interpolated by SAI, while we propose to use SPIA method for detailed part of edgy image. The rest of the pixels for either type of images are interpolated by CBIA. From the simulation results, we found that our adaptive interpolation technique results in better subjective and objective (PSNR) quality in comparision to some of the recent works in literature.

Visual analysis of eye gazes to assist strategic planning in computer games

This work studies the use of a conventional eye tracking system for analysis of an online game players thinking processes. For this purpose, the eye gaze data of several users playing a simple online turn-based checkers game were recorded and made available in real-time to gaze-informed players. The motivation behind this work is to determine if making the eye-gaze data available can help these players to predict the gaze-tracked opponent players further moves, and also how this can be most effectively done. We also tested different orientations of the screen on which the gaze data were displayed. By our visual and algorithmic analysis we validated (1) that prediction is possible and (2) that accuracy highly depends on the moves of players throughout the game as well as on the screen orientation. We believe that our study has implications on visual problem solving in general, especially in collaborative scenarios.

The hierarchical flow of eye movements

Eye movements are composed of spatial and temporal aspects. Moreover, not only the eye movements of one subject are of interest, but a data analyst is more or less interested in the scanning strategies of a group of people in a condensed form. This data aggregation can provide useful insights into the visual attention over space and time leading to the detection of possible visual problems or design flaws in the presented stimulus. In this paper we present a way to visually explore the flow of eye movements, i.e., we try to bring a layered hierarchical structure into the spatio-temporal eye movements. To reach this goal, the stimulus is spatially divided into areas of interest (AOIs) and temporally or sequentially aggregated into time periods or subsequences. The weighted AOI transitions are used to model directed graph edges while the AOIs build the graph vertices. The flow of eye movements is naturally obtained by computing hierarchical layers for the AOIs while the downward edges indicate the hierarchical flow between the AOIs on the corresponding layers.

Color bands: visualizing dynamic eye movement patterns

We introduce a visualization technique called color bands for showing the time-varying eye movement behavior of eye-tracked people. Our contribution is the clutter-free representation of time-varying x- and y-positions of gaze data. We map these coordinates to vertical positions from left to right as in traditional line plots. On top, we display the differences between the x- and y-coordinates by the thickness of the band. Fixation durations are visually encoded as circles of varying diameters. Color coding is used to additionally enhance the distance values and the durations in order to perceptually benefit from pattern recognition for an individual participant but also to compare the eye movement behavior of several participants. We illustrate the usefulness of our technique in a case study investigating eye movements from a formerly conducted eye tracking study on the readability of node-link tree diagrams

Multi-similarity matrices of eye movement data

We describe a matrix-based visualization technique for algorithmically and visually comparing metrics in eye movement data. To reach this goal, a set of scanpath trajectories is first preprocessed andtransformedintoasetofmetricsdescribingcommonalitiesand differences of eye movement trajectories. To keep the generated diagrams simple, understandable, and free of visual clutter we visuallyencodethegenerateddatasetintothecellsofamatrix. Apart from just incorporating one individual metric of the dataset into a matrixcell,weextendthisstandardvisualizationbyadimensionalstackingapproachsupportingthedisplayofseveralofthosemetrics integrated into one matrix cell. To further improve the readability and pattern finding among those values, our approach supports a metric-based clustering and further interaction techniques to manipulate the data and to navigate in it. To illustrate the usefulness of the system, we applied it to an eye movement dataset about the readingbehaviorofmetromaps. Finally,wediscusslimitationsand scalability issues of the approach.