Bit Rate control plays an important role in video coding. Region of Interest (ROI) based rate control has been attracting great attention due to the rapid demands in the region of interest in video coding. The main issue in video coding is the trade off between compression ratio and quality of the reconstructed signal. It is obvious that better quality can be achieved with smaller compression ratio and higher encoded stream bit rate. An optimal coder requires knowledge of the rate distortion (RD) model for the coding scheme. The R-D model is generally built in such a way that the quality of whole frames in a video sequence is taken into account. However in many applications like video monitoring and surveillance, telemedicine, videophone and videoconferencing, some areas in the consecutive frames of the video sequence are more important than others. It is desirable to encode those areas, called region of interest (ROI) with smaller distortion than the rest of the sequence (background). This paper presents a review of the available schemes for bit rate control in region of interest based video coding. Index Terms-Background skipping, bit rate control, content based bit rate allocation, macroblock layer control, ROI video coding, SSIM QP.
The methodology of combining two or more relevant images into a single highly informative image is referred to as image fusion. A new fusion methodology is introduced for combining images obtained from multiple cameras using nonsubsampled shearlet transform (NSST), fuzzy logic and a simple fuzzy neural network (SFNN). The shearlet transform combines the power of multi-scale methods with a unique ability to capture the geometry of multi-dimensional information and is efficient in representing images containing edges. The unique characteristic of shearlets is the utilization of shearing to control directional selectivity, as opposed to rotation utilized by curvelets. The shearlets are not tight edges and therefore it is necessary to perform the synthesis process by iterative methods. A new method, NSST, is introduced for multi-resolution decomposition of input images is introduced. The pixel-based fusion is performed by using fuzzy logic of NSST low-pass coefficients to generate superior quality. The region-based technique is performed by using the SFNN of NSST high-frequency directional coefficients. The SFNN exquisite the set of exemplar input feature vectors and centers a Gaussian function on each remaining one and saves its output label.
In this work, a quantum computing based secret key distribution system is proposed. Quantum gates based encryption and decryption circuits are designed and can be used along with conventional encryption schemes such as the Data Encryption Standards (DES), XoR Cipher and Substitution Cipher etc., in the presentation layer. The quantum communication model is implemented and analyzed for both ideal and noisy channel. Using this model if the channel is eaves dropped, the sender and receiver will detect the eavesdropping attempt, the key will be discarded and another key is retransmitted.
In this paper, we present an evaluation of various wavelet filters in the context of an energy efficient integer wavelet transform on SPARTAN 3E FPGA. The usage of different wavelet filters (Daubechies-9/7, Daubechies-5/3, Haar, etc.) for differential frames is investigated, where we mainly focus on computation time and average image quality of the video stream. We port this software system to a hardware system. Based on wavelet transform, we propose an Energy Efficient Wavelet Image Transform Algorithm (EEIWTA) for lossy compression of video, enabling significant reductions in computation as well as communication energy needed, with minimal degradation in image quality. Additionally, we identify video compression parameters that can be used to effect trade-offs between the energy savings, quality of the image and required communication bandwidth. Finally, the proposed system was implemented. We present energy efficient, adaptive data codec for image and video that can significantly minimize the energy required for wireless image communication, while meeting bandwidth constraints of the wireless networks, as the image quality and latency constraints of the wireless service. In this paper, the optimum method of wavelet transformation is explored. Performance Measure of different Wavelets is compared with and without elimination scheme. Simulation results show the important properties of wavelet, which have to be considered for, image compression. Peak signal to noise ratio (PSNR) is used as a measure to compare wavelet filters. By using these wavelets and compression, we can achieve an optimum balance between the performance metrics like PSNR and Compression Ratio and reduces the Mean Square Error. Our results provide better results in terms of computation time and PSNR ratio.
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