Biblio

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards.

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 60% compared to HEVC.

With the explosive development of big data, it is necessary to sort the data according to their importance or priorities. The sources with different importance levels can be modeled by the multilevel diversity coding systems (MDCS). Another trend in future communication networks, say 5G wireless networks and Internet of Things, is that users may obtain their data from all available sources, even from devices belonging to other users. Then, the privacy of data becomes a crucial issue. In a recent work by Li et al., the secure asymmetric MDCS (S-AMDCS) with wiretap channels was investigated, where the wiretapped messages do not leak any information about the sources (i.e. perfect secrecy). It was shown that superposition (source-separate coding) is not optimal for the general S-AMDCS and the exact full secure rate region was proved for a class of S-AMDCS. In addition, a bound on the key size of the secure rate region was provided as well. As a further step on the SAMDCS problem, this paper mainly focuses on the key size characterization. Specifically, the constraints on the key size of superposition secure rate region are proved and a counterexample is found to show that the bound on the key size of the exact secure rate region provided by Li et al. is not tight. In contrast, tight necessary and sufficient constraints on the secrecy key size of the counterexample, which is the four-encoder S-AMDCS, are proved.

Hadoop is developed as a distributed data processing platform for analyzing big data. Enterprises can analyze big data containing users' sensitive information by using Hadoop and utilize them for their marketing. Therefore, researches on data encryption have been widely done to protect the leakage of sensitive data stored in Hadoop. However, the existing researches support only the AES international standard data encryption algorithm. Meanwhile, the Korean government selected ARIA algorithm as a standard data encryption scheme for domestic usages. In this paper, we propose a HDFS data encryption scheme which supports both ARIA and AES algorithms on Hadoop. First, the proposed scheme provides a HDFS block-splitting component that performs ARIA/AES encryption and decryption under the Hadoop distributed computing environment. Second, the proposed scheme provides a variable-length data processing component that can perform encryption and decryption by adding dummy data, in case when the last data block does not contains 128-bit data. Finally, we show from performance analysis that our proposed scheme is efficient for various applications, such as word counting, sorting, k-Means, and hierarchical clustering.

Advances in virtual reality have generated substantial interest in accurately reproducing and storing spatial audio in the higher order ambisonics (HOA) representation, given its rendering flexibility. Recent standardization for HOA compression adopted a framework wherein HOA data are decomposed into principal components that are then encoded by standard audio coding, i.e., frequency domain quantization and entropy coding to exploit psychoacoustic redundancy. A noted shortcoming of this approach is the occasional mismatch in principal components across blocks, and the resulting suboptimal transitions in the data fed to the audio coder. Instead, we propose a framework where singular value decomposition (SVD) is performed after transformation to the frequency domain via the modified discrete cosine transform (MDCT). This framework not only ensures smooth transition across blocks, but also enables frequency dependent SVD for better energy compaction. Moreover, we introduce a novel noise substitution technique to compensate for suppressed ambient energy in discarded higher order ambisonics channels, which significantly enhances the perceptual quality of the reconstructed HOA signal. Objective and subjective evaluation results provide evidence for the effectiveness of the proposed framework in terms of both higher compression gains and better perceptual quality, compared to existing methods.

GSM network is the most widely used communication network for mobile phones in the World. However the security of the voice communication is the main issue in the GSM network. This paper proposes the technique for secure end to end communication over GSM network. The voice signal is encrypted at real time using digital techniques and transmitted over the GSM network. At receiver end the same decoding algorithm is used to extract the original speech signal. The speech trans-coding process of the GSM, severely distort an encrypted signal that does not possess the characteristics of speech signal. Therefore, it is not possible to use standard modem techniques over the GSM speech channel. The user may choose an appropriate algorithm and hardware platform as per requirement.