Navin Rajpal

A robust image encryption algorithm resistant to attacks using DNA and chaotic logistic maps

An image encryption technique using DNA (Deoxyribonucleic acid) operations and chaotic maps has been proposed in this paper. Firstly, the input image is DNA encoded and a mask is generated by using 1D chaotic map. This mask is added with the DNA encoded image using DNA addition. Intermediate result is DNA complemented with the help of a complement matrix produced by two 1D chaotic maps. Finally, the resultant matrix is permuted using 2D chaotic map followed by DNA decoding to get the cipher image. Proposed technique is totally invertible and it can resist known plain text attack, statistical attacks and differential attacks.

A fast block motion estimation algorithm using dynamic pattern search

In block-based motion estimation algorithms, it has always been desired to reduce search point computation with quality as good as full-search algorithm. A number of such algorithms like diamond search (DS) and hexagon search (HS) have been proposed in literature, which use fixed-size search patterns for finding motion vectors. The drawback with these fixed-size search pattern–based algorithms is that they may suffer from oversearch/undersearch problem depending on the magnitude of the motion vector. In this manuscript, a dynamic pattern search–based algorithm (DPS), which uses spatial and temporal coherence among blocks and dynamically adapts its search pattern for a candidate block, has been proposed. The proposed algorithm has been compared with various motion estimation algorithms like DS, HS, adaptive rood pattern search (ARPS) and full search in terms of various performance parameters. Experimental results show that proposed DPS has a speed gain of 1.18 over ARPS, whereas it is nearly 1.94 and 1.33 over DS and HS algorithms in terms of average search points/block. Further, in terms of peak signal-to-noise ratio (PSNR) (dB)/frame, DPS produces almost same average value than ARPS and HS, whereas it is only 1% inferior to DS. A modified version of DPS has also been proposed, which increases its speed gain by 1.39 times with negligible decrease in PSNR. In terms of another time parameter—average execution time per frame (s)—for DPS, it is 0.66 s, whereas this time is 0.71, 0.77 and 1.06 for ARPS, HS and DS algorithms, respectively.

Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization

This paper presents an imperceptible, robust, secure and efficient image watermarking scheme in lifting wavelet domain using combination of genetic algorithm (GA) and Lagrangian support vector regression (LSVR). First, four subbands low–low (LL), low–high (LH), high–low (HL) and high–high (HH) are obtained by decomposing the host image from spatial domain to frequency domain using one level lifting wavelet transform. Second, the approximate image (LL subband) is divided into non overlapping blocks and the selected blocks based on the fuzzy entropy are used to embed the binary watermark. Third, based on the correlation property of each transformed selected block, significant lifting wavelet coefficient act as target to LSVR and its neighboring coefficients (called feature vector) are set as input to LSVR to find optimal regression function. This optimal regression function is used to embed and extract the scrambled watermark. In the proposed scheme, GA is used to solve the problem of optimal watermark embedding strength, based on the noise sensitivity of each selected block, in order to increase the imperceptibility of the watermark. Due to the good learning capability and high generalization property of LSVR against noisy datasets, high degree of robustness is achieved and is well suited for copyright protection applications. Experimental results on standard and real world images show that proposed scheme not only efficient in terms of computational cost and memory requirement but also achieve good imperceptibility and robustness against geometric and non geometric attacks like JPEG compression, median filtering, average filtering, addition of noise, sharpening, scaling, cropping and rotation compared with the state-of-art techniques.

A robust and efficient image watermarking scheme based on Lagrangian SVR and lifting wavelet transform

In this paper, an efficient image watermarking scheme based on Lagrangian support vector regression (LSVR) and lifting wavelet transform (LWT) is proposed to balance the trade-off between imperceptibility and robustness. LWT is faster and efficient implementation of traditional wavelet transform and LSVR has faster learning speed and high generalization capability compared to classical support vector regression. Combination of LWT and LSVR based image watermarking not only show imperceptibility and robustness but also reduces the time complexity. First the image is decomposed into four subbands using one level LWT and low frequency subband (approximate image) is partitioned into non overlapping blocks. Selected blocks based on correlation of wavelet coefficients are used to embed the binary watermark logo. For each selected block, significant wavelet coefficient is used as target to LSVR and its neighboring coefficients (feature vector) act as input to LSVR. The security of the watermark is achieved by applying Arnold transformation to original watermark. The scrambled watermark bit is embedded by comparing the output (predicted value) of LSVR of each selected block and the actual target value. The good learning capability and high generalization property of LSVR against noisy datasets provides the successful extraction of watermark against several image processing attacks: median filtering, average filtering, addition of Gaussian noise, salt and pepper noise, contrast enhancement, blurring, scaling, cropping and rotation. Experimental results show that the proposed scheme gives significant improvement in imperceptibility and robustness compared to the state-of-art techniques.

A hybrid semi-blind gray scale image watermarking algorithm based on DWT-SVD using human visual system model

To achieve good imperceptibility and robustness, a hybrid image watermarking algorithm based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed using the characteristics of human visual system model for copyright protection and authenticity. In the proposed watermarking algorithm, one level DWT is applied to selected image blocks to obtain four sub-bands of each block and then the U component of low frequency sub-band (LL) obtained after SVD transformation is explored under different threshold values for embedding and extracting the watermark. The experimental results show that HVS model based hybrid image watermarking scheme is imperceptible and robust against several image processing operations like JPEG compression, median filtering, sharpening, cropping and addition of Gaussian noise. Peak signal to noise ratio (PSNR) and bit correction rate (BCR) are used to measure the quality of watermarked image and extracted watermark respectively.

LWT- QR decomposition based robust and efficient image watermarking scheme using Lagrangian SVR

In this paper, an efficient and robust image watermarking scheme based on lifting wavelet transform (LWT) and QR decomposition using Lagrangian support vector regression (LSVR) is presented. After performing one level decomposition of host image using LWT, the low frequency subband is divided into 4 × 4 non-overlapping blocks. Based on the correlation property of lifting wavelet coefficients, each selected block is followed by QR decomposition. The significant element of first row of R matrix of each block is set as target to LSVR for embedding the watermark. The remaining elements (called feature vector) of upper triangular matrix R act as input to LSVR. The security of the watermark is achieved by applying Arnold transformation to original watermark to get its scrambled image. This scrambled image is embedded into the output (predicted value) of LSVR compared with the target value using optimal scaling factor to reduce the tradeoff between imperceptibility and robustness. Experimental results show that proposed scheme not only efficient in terms of computational cost and memory requirement but also achieve good imperceptibility and robustness against image processing operations compared to the state-of-art techniques.

Digital Image Watermarking in DCT Domain Using Finite Newton Support Vector Regression

Finite Newton Support Vector Regression (FNSVR) is a reliable and robust method for regression analysis which unlike other support vector regression methods, converges the computation in a few iterations. In this paper, we have used FNSVR method for embedding and extracting a binary image as a watermark in 8-bit gray scale cover images. Digital image watermarking being a time consuming task, requires fast embedding and extraction procedures and overall it should have a low time complexity. FNSVR being an algorithm which consumes less number of iterations, embeds a given watermark in a short time span. Computed PSNR values indicate good quality of the signed images. parameter is computed and its values indicate that the extraction process is quite successful.

A matching criterion for motion compensation in the temporal coding of video signal

Video data have spatial as well as temporal redundancy and motion estimation plays a vital role in the removal of temporal redundancy of video data. Block matching techniques are mostly used and generally the matching criterion in these block matching techniques is the mean absolute error (MAE). Though MAE-based approach is simple and less complex, it does not give better prediction specially for large motion video inputs with contrast variation. In this manuscript, a new block matching criterion has been suggested and experimentally compared with three existing methods in terms of four parameters—average MAE/pixel, average search points/block, average peak signal to noise ratio (PSNR) and average number of bits/pixel value. Proposed criterion gives nearly 75% less average error than conventional MAE. An increase of nearly 16% in average PSNR value and 37% reduction in average bits/pixel value in comparison to MAE has been observed for the proposed criterion. Further, these criterions have also been evaluated for quality/compression ratio which is nearly 80% more for the proposed criterion than corresponding MAE metric.

Reconstruction of curves from point clouds using fuzzy logic and ant colony optimization

A new approach based on fuzzy logic and ant colony optimization is presented for the reconstruction of curves from a set of unorganized points. Fuzzy clustering is used to reduce the number of points to cluster centres. Ant colony optimization is used to construct a travelling salesman path which is a closed curve. Extra edges are deleted and new edges are added using the fuzzy membership function. The algorithm presented in this paper has been used for reconstructing open as well as closed curves. The results obtained for multiple and self-intersecting curves are also good. Various examples for open, closed, multiple and intersecting curves with complicated shapes are shown to illustrate the significance of the presented algorithm.

Improved fuzzy transform based image compression and fuzzy median filter based its artifact reduction: pairFuzzy

During image compression, visually significant edges should be well preserved for human perception. Despite existence of many image compression standards, joint photographic experts group (JPEG) is the most popularly used standard for image compression. However at low bit rate, JPEG compressed images exhibit blocking artifacts that adversely affect the visual image quality. Thus, to produce a high visual quality image at low bit rate, pairFuzzy algorithm that is simple and more efficient as compared to JPEG alongwith the capability of reducing artifact is proposed. The proposed algorithm is carried out in three steps. First, an image is preprocessed using competitive fuzzy edge detection which efficiently detects the edge pixels contained in the image. Second, based on the edge information the image is compressed and decompressed using improved fuzzy transform. Third, the reconstructed image is postprocessed using fuzzy switched median filter for artifact reduction. The subjective as well as objective analysis alongwith the comparison to recent methods proves the superiority of proposed algorithm.