A Study on Efficient Design of A Multimedia Conversion Module in PESMS for Social Media Services

The main contribution of this paper is to present the Platform-as-a-Service(PaaS) Environment for Social Multimedia Service (PESMS), derived fromthe Social Media Cloud Computing Service Environment. The main role ofour PESMS is to support the development of social networking services thatinclude audio, image, and video formats. In this paper, we focus in particular on the design and implementation of PESMS, including the transcoding function for processing large amounts of social media in a parallel and distributed manner. PESMS is designed to improve the quality and speed of multimedia conversions by incorporating a multimedia conversion module based on Hadoop, consisting of Hadoop Distributed File System for storing large quantities of social data and MapReduce for distributed parallel processing of these data. In this way, our PESMS has the prospect of exponentially reducing the encoding time for transcoding large numbers of image files into specific formats. To test system performance for the transcoding function, we measured the image transcoding time under a variety of experimental conditions. Based on experiments performed on a 28-node cluster, we found that our system delivered excellent performance in the image transcoding function

Three-dimensional photon counting optical encryption with enhanced visual quality and security level

In this paper, we propose three-dimensional (3D) photon counting double random phase encryption (DRPE) with enhanced visual quality and security level. Conventional 3D photon counting DRPE can quickly encrypt the data by using the 4f optical system and random phase masks with enhanced security because the 3D photon counting technique is used. 3D photon counting DRPE extracts photons from the encrypted data by using statistical methods such as the Poisson random process, and the visual quality of the decrypted data can be enhanced through the 3D reconstruction process. However, it still has a problem to visualize the data when we extract extremely a few photons. To improve the security and the visual quality of the decrypted data, we propose the random amplitude reconstruction process in the encryption stage. Our proposed method reconstructs the amplitude of the encrypted data at a random depth. Thus, the shifting pixel value and depth information can be another important key for decryption through the random process. Therefore, it can effectively decrypt data securely, and the visual quality of the decrypted data can be enhanced. Finally, through the random reconstruction process in the decryption stage, our proposed method can simultaneously enhance the security and visual quality. To verify our proposed method, we carry out the simulation and optical experiment

3D Visualization of objects in heavy scattering media by using wavelet peplography

In this paper, we propose three-dimensional(3D) visualization of objects in heavy scattering media by using peplography and wavelet transform. Conventional haze removal techniques can remove the light haze in the image by using various image processing algorithms or machine learning techniques. However, they may not provide a clear image under heavy scattering media. On the other hand, peplography can visualize the object by detecting ballistic object photons from heavy scattering media. Then, 3D image can be generated by integral imaging. However, it may not visualize 3D object information accurately because of the noise photons from scattering media. Therefore, the image quality for 3D object visualization may be degraded. To solve this problem, we use the discrete wavelet transform in peplography. It can detect the object photon signals from the scattering media and enhance 3D image contrast ratio by using a specific coefficient threshold technique. To prove our method, we carry out optical experiments and compare results with the conventional haze removal method and peplography by using various image quality metrics such as correlation, structural similarity, and peak signal-to-noise ratio

3D Visualization for Extremely Dark Scenes Using Merging Reconstruction and Maximum Likelihood Estimation

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments