Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Wireless sensor networks (WSNs) have long been established as a suitable technology for gathering and processing information\nfrom the environment. However, recent applications and new multimedia sensors have increased the demand for a more\nadequate management of their quality of service (QoS). The constraints and demands for this QoS management greatly depend\non each individual networkâ??s purpose or application. Low-Energy Adaptive Clustering Hierarchy (LEACH) is arguably the most\nwell-known routing protocol for WSNs, but it is not QoS-aware. In this paper, we propose LEACH-APP, a new clustering\nprotocol based on LEACH that takes the networkâ??s application into account and is aimed at providing a better overall QoS\nmanagement. We thoroughly describe our proposal and provide a case study to explain its operation. Then, we evaluate its\nperformance in terms of two significant QoS metrics-throughput and latency-and compare it to that of the original protocol.\nOur experiments show that LEACH-APP increases the throughput by roughly 250% and reduces the latency by almost 80%,\noverall providing a more flexible and powerful QoS management....
With the wide use of various image altering tools, digital image manipulation becomes\nvery convenient and easy, which makes the detection of image originality and authenticity significant.\nAmong various image tampering detection tools, double JPEG image compression detector, which\nis not sensitive to specific image tampering operation, has received large attention. In this paper,\nwe propose an improved double JPEG compression detection method based on noise-free DCT\n(Discrete Cosine Transform) coefficients mixture histogram model. Specifically, we first extract the\nblock-wise DCT coefficients histogram and eliminate the quantization noise which introduced by\nrounding and truncation operations. Then, for each DCT frequency, a posterior probability can\nbe obtained by solving the DCT coefficients mixture histogram with a simplified model. Finally,\nthe probabilities from all the DCT frequencies are accumulated to give the posterior probability\nof a DCT block being authentic or tampered. Extensive experimental results in both quantitative\nand qualitative terms prove the superiority of our proposed method when compared with the\nstate-of-the-art methods....
MCJ2K (Motion-Compensated JPEG2000) is a video codec based on MCTF (Motion-\nCompensated Temporal Filtering) and J2K (JPEG2000). MCTF analyzes a sequence of images,\ngenerating a collection of temporal sub-bands, which are compressed with J2K. The R/D\n(Rate-Distortion) performance in MCJ2K is better than the MJ2K (Motion JPEG2000) extension,\nespecially if there is a high level of temporal redundancy. MCJ2K codestreams can be served by\nstandard JPIP (J2K Interactive Protocol) servers, thanks to the use of only J2K standard file formats.\nIn bandwidth-constrained scenarios, an important issue in MCJ2K is determining the amount of data\nof each temporal sub-band that must be transmitted to maximize the quality of the reconstructions\nat the client side. To solve this problem, we have proposed two rate-allocation algorithms which\nprovide reconstructions that are progressive in quality. The first, OSLA (Optimized Sub-band Layers\nAllocation), determines the best progression of quality layers, but is computationally expensive.\nThe second, ESLA (Estimated-Slope sub-band Layers Allocation), is sub-optimal in most cases, but\nmuch faster and more convenient for real-time streaming scenarios. An experimental comparison\nshows that even when a straightforward motion compensation scheme is used, the R/D performance\nof MCJ2K competitive is compared not only to MJ2K, but also with respect to other standard scalable\nvideo codecs....
In this study, an edge-preserving nonlinear filter is proposed to reduce multiplicative\nnoise by using a filter structure based on mathematical morphology. This method is called the\nminimum index of dispersion (MID) filter. MID is an improved and extended version of MCV\n(minimum coefficient of variation) and MLV (mean least variance) filters. Different from these\nfilters, this paper proposes an extra-layer for the value-and-criterion function in which orientation\ninformation is employed in addition to the intensity information. Furthermore, the selection function\nis re-modeled by performing low-pass filtering (mean filtering) to reduce multiplicative noise. MID\noutputs are benchmarked with the outputs of MCV and MLV filters in terms of structural similarity\nindex (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (MSE), standard deviation,\nand contrast value metrics. Additionally, F Score, which is a hybrid metric that is the combination\nof all five of those metrics, is presented in order to evaluate all the filters. Experimental results\nand extensive benchmarking studies show that the proposed method achieves promising results\nbetter than conventional MCV and MLV filters in terms of robustness in both edge preservation\nand noise removal. Noise filter methods normally cannot give better results in noise removal and\nedge-preserving at the same time. However, this study proves a great contribution that MID filter\nproduces better results in both noise cleaning and edge preservation....
The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over\nHTTP is an efficient scheme for bitrate adaptation inwhich video is segmented and stored in different quality levels.Themultimedia\nstreaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We\nhave proposed an adaptive rate control using enhancedDoubleDeep Q-Learning approach to improvemultimedia content delivery\nby switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly\nevaluated against state-of-the-art heuristic and learning-based algorithms.The performance metrics such as PSNR, SSIM, quality\nof experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which\nshows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm\nprovides faster convergence to the optimal solution as compared to other algorithms considered in our work....
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