Showing papers on "Peak signal-to-noise ratio published in 2013"

A Secure And High Capacity Image Steganography Technique

Abstract: Steganography is the science of invisible communication. The purpose of Steganography is to maintain secret communication between two parties. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide multiple secret images and keys in color cover image using Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted secret images are also similar to the original secret images. Very good PSNR (Peak Signal to Noise Ratio) values are obtained for both stego and extracted secret images. The results are compared with the results of other techniques, where single image is hidden and it is found that the proposed technique is simple and gives better PSNR values than others.

Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms

Abstract: In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm-based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques.

A Secure and High Capacity Image Steganography Technique

Abstract: Steganography is the science of “invisible” communication. The purpose of Steganography is to maintain secret communication between two parties. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide multiple secret images and keys in color cover image using Integer W avelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted secret images are also similar to the original secret images. Very good PSNR (Peak Signal to Noise Ratio) values are obtained for both stego and extracted secret images. The results are compared with the results of other techniques, where single image is hidden and it is found that the proposed technique is simple and gives better PSNR values than others

Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure?

Abstract: In this study, the authors analyse two well-known image quality metrics, peak-signal-to-noise ratio (PSNR) as well as structural similarity index measure (SSIM), and the authors derive an analytical relationship between them which works for some kinds of common image degradations such as Gaussian blur, additive Gaussian noise, Jpeg and Jpeg2000 compressions. The analytical relationship brings more clarity on the interpretation of PSNR and SSIM values, explains some differences found between these quality measures in the literature and confirms some experimental observations regarding these measures. A series of tests realised on images from the Kodak database give a better understanding of the performance of SSIM and PSNR in assessing image quality.

A Secure Color Image Steganography in Transform Domain

Abstract: Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.

An Improved Invisible Watermarking Technique for Image Authentication

Abstract: Digital Watermarking is a technique which embeds a watermark signal into the host image in order to authenticate it. In their previous work, a binary watermark pattern was constructed from the information content of the image by selecting the minimum value from every block of size 2x2, and was disordered with the help of Arnold Transform but which was not showing a fine robustness against compression and rotation operation. As a solution to this issue, an innovative watermarking scheme is proposed. According to this, the low frequency sub-band of wavelet domain and the rescaled version of original image are utilized in the watermark construction process. A scrambled version of watermark is obtained with the help of Arnold Transform. The operation of embedding and extraction of watermark is done in high frequency domain of Discrete Wavelet Transform since small modifications in this domain are not perceived by human eyes. This watermarking scheme deals with the extraction of the watermark information in the absence of original image, hence the blind scheme was obtained. Peak Signal to Noise Ratio (PSNR) and Similarity Ratio (SR) are computed to measure image quality. In addition, the competency of the proposed method is verified under common image processing operations and a comparative study is made against the previous technique.

Salt and Pepper Noise Detection and Removal in Gray Scale Images: An Experimental Analysis

Abstract: Impulse noise removal is a mechanism for detection and removal of impulse noise from images. Median filters are preferred for removing impulse noise because of their simplicity and less computational complexity. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. Extensive simulations have been carried out on a set of standard gray scale images and the state of the art median filter variants are compared in terms of the well known image quality assessment metrics namely mean square error, peak signal to noise ratio and multiscale structural similarity index.

Particle Swarm Optimization based Contrast Limited enhancement for mammogram images

Abstract: In the present medical scenario detection of breast cancer in its early stage is a very immense challenge. Many histogram based enhancement are present today. In this paper a Particle Swarm Optimization (PSO) for tuning the enhancement parameter of Contrast Limited Adaptive Histogram Equalization (CLAHE) based on Local Contrast Modification (LCM) is presented. The PSO method of parameter tuning adopted for LCM-CLAHE enhancement for mammogram images achieves very good quality of images compared to other exiting methods. The quality of enhanced image is tested using an efficient objective criteria based on entropy and edge information of the image. Results are compared with other enhancement techniques such as histogram equalization, unsharpmasking. The performance of this method is tested using Peak Signal to Noise Ratio. The quality of image shows that image obtained after this method can be useful for efficient detection of breast cancer in further process like segmentation, classification etc.

No-Reference PSNR Estimation for HEVC Encoded Video

Abstract: Video quality estimation is considered a means of monitoring quality of service in broadcasting or IPTV services. In this paper, a no-reference peak signal-to-noise ratio (PSNR) estimation method is first presented for a quadtree-based motion estimation or compensation and transform coding scheme such as HEVC test model (HM), which is expected to be popularly used due to its highly enhanced coding efficiency, in 2-D and 3-D high resolution videos. The proposed no-reference PSNR estimation method is based on a Laplacian mixture distribution, which takes into account the distribution characteristics of residual transform coefficients in different quadtree depths and coding types of coding units (CUs). In order to predict the model parameters of the Laplacian mixture distribution for all zero quantized coefficients case, an exponential regression scheme is employed over quadtree depth levels of CUs. The proposed no-reference PSNR estimation method yields fairly accurate results from 0.970 to 0.983 in correlation and from 0.530 to 0.890 in RMSE between the actual and the estimated PSNR values for HM encoded bitstreams, outperforming single PDF based models.

Implementation of image steganography using 2-level DWT technique

Abstract: Image steganography is an engineering term defining a different and significant discipline for information hiding. This process can be described as ‘hiding of secret information behind an image’. Discrete wavelet Transform (DWT) is one of the known methods under steganography. The focus of the proposed work in this paper is on decreasing the complexity in image hiding through DWT technique while providing better undetectability and lesser distortion in the stego image. This paper proposes the algorithm for embedding and extracting the secret image embedded behind the cover gray scale image. Also, the analysis of performance measurement methods such as Peak signal to noise ratio (PSNR) and Mean square error (MSE), gives us the experimental summary for four different cases where each cases spans different sizes of cover and secret image, comparing the cover image & stego image at the sender’s side and embedded secret & extracted secret at the receiver’s side. The stego attacks are then applied on the stego image and after each of the attack, the secret image is extracted from the distorted image. For better analysis, this extracted secret is compared with the expected result on the basis of PSNR and MSE. Also, the proposed algorithm is compared with one of the existing method using DWT technique, proposed by K.B. Shiva Kumar et. al. and hence the conclusion is drawn.

Efficient adaptive prediction based reversible image watermarking

Abstract: In this paper, we propose a new reversible watermarking algorithm based on additive prediction-error expansion which can recover original image after extracting the hidden data. Embedding capacity of such algorithms depend on the prediction accuracy of the predictor. We observed that the performance of a predictor based on full context prediction is preciser as compared to that of partial context prediction. In view of this observation, we propose an efficient adaptive prediction (EAP) method based on full context, that exploits local characteristics of neighboring pixels much effectively than other prediction methods reported in literature. Experimental results demonstrate that the proposed algorithm has a better embedding capacity and also gives better Peak Signal to Noise Ratio (PSNR) as compared to state-of-the-art reversible watermarking schemes.

Image resolution enhancement by using interpolation followed by iterative back projection

Abstract: In this paper, we propose a new super resolution technique based on the interpolation followed by registering them using iterative back projection (IBP). Low resolution images are being interpolated and then the interpolated images are being registered in order to generate a sharper high resolution image. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results as well as the visual results show the superiority of the proposed technique over the conventional and state-of-art image super resolution techniques. For Lena's image, the PSNR is 6.52 dB higher than the bicubic interpolation.

A technique for digital watermarking in combined spatial and transform domains using chaotic maps

Abstract: In this paper, the problems of robustness and quantity of embedded watermark of digital watermarking linked with independent spatial and frequency domains have been analysed. In attempt to overcome these problems to some extent, we have proposed a technique for watermarking in combined spatial and frequency domains based upon chaotic maps. By applying chaos effectively in secure communication, the strength (robustness) of overall anticipated algorithm has been increased to a significant level. In addition, few security statistical analyses such as correlation, entropy, energy, contrast, homogeneity, mean square error and peak signal to noise ratio have also been carried out and it is shown through confidence measure that it can survive against unintentional attacks such as addition of noise, compression and cropping.

Visible watermarking within the region of non -interest of medical images based on fuzzy c-means and harris corner detection

Abstract: Transfer of medical information amongst various hospitals and diagnostic centers for mutual availability of diagnostic and therapeutic case studies is a very common process. Watermarking is adding “ownership” information in multimedia contents to verify signal integrity, prove authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment. Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial for adding watermark. First part of the present work proposes proper selection of Region of Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve retention of diagnostic value lost in embedding ownership information. The second part of the work presents watermark embedding in the selected area of RONI based on alpha blending technique. In this approach, the generated watermarked image having an acceptable level of imperceptibility and distortion is compared to the original image. The Peak Signal to Noise Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of the proposed method.

A Graph Theory Practice on Transformed Image: A Random Image Steganography

Abstract: Modern day information age is enriched with the advanced network communication expertise but unfortunately at the same time encounters infinite security issues when dealing with secret and/or private information. The storage and transmission of the secret information become highly essential and have led to a deluge of research in this field. In this paper, an optimistic effort has been taken to combine graceful graph along with integer wavelet transform (IWT) to implement random image steganography for secure communication. The implementation part begins with the conversion of cover image into wavelet coefficients through IWT and is followed by embedding secret image in the randomly selected coefficients through graph theory. Finally stegoimage is obtained by applying inverse IWT. This method provides a maximum of 44 dB peak signal to noise ratio (PSNR) for 266646 bits. Thus, the proposed method gives high imperceptibility through high PSNR value and high embedding capacity in the cover image due to adaptive embedding scheme and high robustness against blind attack through graph theoretic random selection of coefficients.

Removal of fixed value impulse noise using improved mean filter for image enhancement

Abstract: In this paper we have introduced a new method for the enhancement of gray scale images, when images are corrupted by fixed valued impulse noise (salt and pepper noise). Our proposed method gives a better output for low-density impulse noise as compare to the other famous filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF) and Modified Decision Based Median Filter (MDBMF) and so on. In our proposed method we have improved the Image Enhancement factor (IEF), Peak signal to noise ratio (PSNR), visual perception and also reduce blurring in the image. The proposed algorithm replaces the noisy pixel by trimmed mean value. When previous pixel values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remain noisy pixels are replaced by mean value. Different gray-scale images are tested via proposed method. The experimental result shows better Peak Signal to Noise Ratio (PSNR) value, Image Enhancement Factor (IEF) and with better visual and human perception.

A hybrid DCT-SVD based robust watermarking scheme for copyright protection

Abstract: A hybrid watermarking scheme exploiting the properties of the Discrete Cosine Transform (DCT) and Singular Value Decomposition(SVD) has been proposed here. A reference image is being formed from the cover image and then its singular values are modified to hide the secret information in an imperceptible way. The security is further enhanced by the zig zag scrambling of the cover image and gray scale watermarks. The robustness of the methodology against the various image processing attacks has been validated with high Normalized Cross Correlation (NCC) values. Also, the imperceptibility of the watermarked image with the original cover image comes out to be high as indicated by high achievable Peak Signal to Noise Ratio (PSNR) values.

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

Abstract: 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.

Design and implementation of TDBLMS adaptive filter and comparison of PSNR values of various de-noised images

Abstract: This paper is based on the compare of peak signal to noise ratio (PSNR) value of different types of noisy images. Noisy images are filtered by two dimensional block least mean square adaptive filter algorithm (TDBLMS). In this paper, adaptive filter technique apply to improve the quality of images, such as Lena image, baboon image, mammography image and ultrasound image. These images are corrupted by additive noise and multiplicative noise, like Gaussian, Poisson, Speckle etc. Proposed algorithm of an image, noise can achieved of input images by an adaptive filter. In this adaptive filter, each image is processed the block-by-block operations with the smaller block size (2×2, 4×4, 8×8 etc.) are applied to the original noisy image for getting the suitable weight matrix that will be used as the initial one for the block-adaptation phase such that a higher signal-to-noise ratio can be achieved. The simulation result shows that the quality of image is better than input image with the help of calculated PSNR values.

Application of Steganography in Symmetric Key Cryptography with Genetic Algorithm

Abstract: Embedding maximum information in a stego-image with minimum change in its appearance has been a major concern in image-based steganography techniques. In this paper, utilizing Genetic algorithm (GA) we have built up a competent optimal robust steganography technique. The watermarks are implanted into the HL and LH frequency coefficients in bi-orthogonal wavelet transform (BWT). In order to get better the quality of stego image and robustness of the steganography we have to develop an optimization technique using a model to explore for the optimal locations. We examine the presentation of the suggested technique in terms of Peak signal to Noise ratio (PSNR) and Normalized correlation (NC). The proposed technique is the application of steganography for confidential transmission of symmetric key generated using Genetic algorithm (GA) that can accomplish a good imperceptibility and robustness of the image

Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

Abstract: In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detec- tion, even if the watermarked image has been distorted. To recognize the selected object region after geometric dis- tortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF fea- tures of the distorted image are estimated and matched with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The re- ceiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided.

Computer Assisted Diagnostic System in Tumor Radiography

Abstract: An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.

Adaptive image denoising using wavelet thresholding

Abstract: In order to preserve fine details in image denoising, we propose a scheme by assuming that the deviations of the noisy and the original wavelet coefficients of image are not always the same across the scales. The proposed algorithm considers not only the correlation of inter-scale wavelet coefficients but also the mentioned assumptions. In the process of denoising, the proposed denoising threshold can adaptively adjust itself on the basis of its position and decomposition scale. We demonstrate its effectiveness through simulations with images contaminated by additive white Gaussian noise and compare it with the classical threshold method. Experimental results show that the performance of our method can preserve image details well both in visual effect and in terms of peak signal-to-noise ratio.

A 4-quadrant Curvelet Transform for Denoising Digital Images

Abstract: The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.

Selective region based images encryption

Abstract: Image security is of great importance in many applications including Military, Medical and many others. Generally, images are of very large sizes and conventional encryption techniques are not considered good. An approach that is very recently found in literature is of selective encryption of multimedia images. In this paper a new approach is introduced for the selection of the area in the image that will select maximum information area for encryption based on percentage of coefficients. Un-encrypted area is permuted with the encrypted area that will further enhance the security of the images. The mean square error (MSE) and peak signal to noise ratio (PSNR) values show a huge difference in between the original and encrypted images.