An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

Most remote systems require user authentication to access resources.Text-based passwords are still widely used as a standard method of user authentication.Although conventional text-based passwords are rather hard to remember,users often write their passwords down in order to compromise security.One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users.Graphical user authentication methods have recently been proposed to verify the user identity.However,the fundamental limitation of a graphi-cal password is that it must have a colorful and rich image to provide an adequate password space to maintain security,and when the user clicks and inputs a pass-word between two possible grids,the fault tolerance is adjusted to avoid this situation.This paper proposes an enhanced graphical authentication scheme,which comprises benefits over both recognition and recall-based graphical techniques besides image steganography.The combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user accounts.To evaluate the effectiveness of the proposed scheme,peak signal-to-noise ratio and mean squared error parameters have been used.

An effective fractal image compression algorithm based on plane fitting

A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. Then, the best-matched one for every range block is obtained by analysing the relation between their coefficients. Experimental results show that the proposed method can shorten encoding time markedly, while the retrieved image quality is still acceptable. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce blocking effects. At the same time, the proposed method can also achieve a high compression ratio.

Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm

A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.

Video quality based link adaptation for low latency video transmission over WLANs

Wireless Local Area Networks (WLANs) such as IEEE 802.11a/g and Hiperlan/2 utilise numerous transmission modes, each providing different throughputs and reliability levels. Many link adaptation algorithms proposed in the literature either maximise the error-free data throughput based on channel conditions or are based on the number of failed transmissions. However, these algo- rithms do not take into account the content of the data stream and strongly rely on the use of Automatic Repeat Requests (ARQs). Low latency video applications such as real-time video transmission may require no retransmission, or only a limited number of retrans- missions. Moreover, completely error-free communication is not essential, especially if robust video compression techniques are applied. In such scenarios, improved decoded video quality can be obtained with a video stream transmitted at a higher bit rate using a higher link speed but with some degree of transmission error, rather than an error-free video stream at a lower bit rate using a lower link speed. In this work, we investigate a link adaptation scheme that improves the Quality of Service (QoS) for video transmission, based on the overall received video quality (Peak Signal to Noise Ratio, PSNR), rather than by maximising the error-free throughput. We also study a practical link adaptation approach that uses PER thresholds at the PHY layer. An empirical study showed that thresholds for switching from one mode to another are much lower (almost error free) than those currently used by throughput based schemes. We show that traditional link adaptation strategies are not appropriate for real-time video transmission with no retransmis- sion. Simulation results using the H.264 video compression standard over IEEE 802.11a are presented.

Improved Diamond Search Algorithm for H.264/AVC Video Coding Standard

Diamond search （DS） is an excellent fast block matching motion estimation （BMME） algorithm. In this paper, we propose an improved diamond search （IDS） algorithm, which revises the two search patterns of DS. The proposed algorithm is compared with several mainstream algorithms. The simulation results show that the proposed algorithm over DS can be up to 20% gain on speedup on average, while maintain the similar or even better quality, both objectively and subjectively. The proposed algorithm is also competitive with other fast algorithms.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

A Two-Layer Real-Time Rate Control Algorithm Based on Lagrange Optimum Algorithm

Because of the iterative process in the rate control algorithm of the H.264 draft, it is not suitable for real-time transmission. A two-layer real-time rate control algorithm based on the Lagrange optimum algorithm is proposed. This algorithm consists of two layers, namely, the group of pictures （GOP） layer and the frame layer. The simulation results show that the proposed algorithm is competitive in terms of peak signal-to-noise ratio （PSNR）, real bitrates and the Q-value among frames over the original algo- rithm.

A combination of learning and non-learning based method for enhancement, compression and reconstruction of underwater images

Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.