Ehsan Akhtarkavan

Multiple Descriptions Coinciding Lattice Vector Quantizer for Wavelet Image Coding

Multiple description (MD) coding has been a popular choice for robust data transmission over the unreliable network channels. Lattice vector quantization provides lower computation for efficient data compression. In this paper, a new MD coinciding lattice vector quantizer (MDCLVQ) is presented. The design of the quantizer is based on coinciding 2-D hexagonal sublattices. The coinciding sublattices are geometrically similar sublattices, with the same index but generated by different generator matrices. A novel labeling algorithm based on the hexagonal coinciding sublattices is also developed. Performance results of the MDCLVQ scheme, together with the new labeling algorithm applied to standard test images, show improvements of the central and side decoders, as compared with the renowned techniques for several test images.

Multiple description lattice vector quantization using multiple A4 quantizers

This paper presents a new Multiple Description Lattice Vector Quantization (MDLVQ) based on A4 lattice quantizers for multiple description coding abbreviated as MDLVQ-A4. The Coinciding similar A4 sublattices are used to quantize the input stream for multiple description coding system. The use of multiple coinciding quantizers eliminates the labeling function of the traditional schemes. Experimental results of MDLVQ-A4 scheme for image coding show a higher performance in terms of PSNR of the side decoders as compared to the renowned techniques of image coding for several test images.

A image steganography scheme based on discrete wavelet transform using lattice vector quantization and reed-solomon encoding

Steganography is art, a science or a practical attempt that through it some information can be hidden in a cover. The cover can be in audio, image, etc. formats. In this research, a new method for steganography based on lattice vector quantization and DWT is introduced that provides a suitable security level in addition to the hiding possibility as well as complete and accurate extraction of data and makes appropriate level of security by using error correction coding Reed-Solomon (63, 11). The proposed method, using the 3-level lifting wavelet transform and Blocking and then embedding in selected coefficients with using lattice vector quantization, greatly reduces the likelihood of success of mining hidden. The results show that the assessment indicators PSNR, MSE and SSIM better and more appropriate than the same methods and have the previous.

Multiple Descriptions Coinciding Lattice Vector Quantizer for H. 264/AVC and Motion JPEG2000

Recent advances in high-performance portable processing equipment, such as mobile pro‐ cessors, have enabled users to experience new-generation devices, including networked gaming consuls, smart televisions and smart phones. Video coding, video compression and video communication are essential parts of the aforementioned applications. However, networking infrastructures do not offer unlimited bandwidth, and storage devices do not offer unlimited capacities. Therefore, there is significant demand for reliable high-performance video communication/compression protocols. Video compression refers to the process of reducing the amount of video data used to represent digital videos; it is a combination of spatial image compression and temporal motion compensation (Hanzo et al., 2007).

Multiple descriptions coding for H.264/AVC using coinciding A 2 lattice vector quantizer

Applications involving multimedia communications are widespread. However available networks do not meet the users needs such as unlimited bandwidth and reliability. Therefore video compression techniques are used to decrease the size of the video data and error resiliency techniques are used to combat against channel failures. Multiple Descriptions Lattice Vector Quantization (MDLVQ) is a technique that combines these techniques and suits for robust data transmission over unreliable network channels. Multiple Descriptions Coinciding Lattice Vector Quantization (MDCLVQ) is a new MDLVQ scheme based on the coinciding sublattices of A2 lattice. In this paper MDCLVQ has been employed in order to form an MD coding scheme for H.264/AVC video coding standard to increase the robustness of video transmission over error-prone communication channels. The proposed MD video coding scheme is applied to several reference video sequences. The experimental results show that the encoding performance of the scheme is increased while the average PSNR of the central decoder remains above 33.84. It means that error resiliency of the scheme is increased without significant drop in the reconstruction quality. In addition the proposed scheme is compared with renowned techniques. It is observed that the proposed scheme outperforms all the other algorithms in low bit rate regime.

Multiple description lattice vector quantization using Coinciding similar lattices of A 4

Lattice Vector Quantization (LVQ) has been very popular in image compression since it needs lower computations and offers high performance. This paper presents a new multiple A4 lattice based coinciding quantization scheme for multiple description coding (MDLVQ — A4). The Coinciding similar A4 sublattices are used to quantize the input stream for multiple description coding system. The use of multiple coinciding quantizers eliminates the need for labeling function as in the traditional schemes. The MDLVQ — A4 scheme is applied to image coding to evaluate the performance. Experimental results of the MDLVQ — A4 scheme show a higher performance in terms of PSNR of the side decoders as compared to the renowned techniques of wavelet image coding and optimized wavelet image coding for several test images.

Energy Adaptive Cluster-Head Selection for Wireless Sensor Networks Using Center of Energy Mass

A set of small battery-operated sensors with low-power transceivers that can automatically form a network and collect some desired physical characteristics of the environment is called a wireless sensor network. The communications must be designed to conserve the limited energy resources of the sensors [14].By clustering sensors we can save energy. In this paper, we introduce a new concept called “Center of Energy Mass” which is a combination of both energy level and location of the nodes which is used to form the new factor of “distance of the nodes to the CEM “.Distance of the nodes to the CEM is used together with Probability Density Function of the normal distribution in optimizing LEACH’s cluster head selection algorithm. We optimized LEACH’s random Cluster-Heads selection algorithm by means of finding the CEM, to ensure balanced energy depletion over the whole network thus prolonging the network lifetime. Simulation results show that our algorithm improves First Node Dies by 23.5% and Half Nodes Die by 5.6%.