High efficiency video coding (HEVC)

No abstract

Recently, High-Efficiency Video Coding (HEVC/H.265) has been chosen to replace previous video coding standards, such as H.263 and H.264. Despite the efficiency of HEVC, it still lacks reliable and practical functionalities to support authentication and copyright applications. In order to provide this support, several watermarking techniques have been proposed by many researchers during the last few years. However, those techniques are still suffering from many issues that need to be considered for future designs. In this paper, a Systematic Literature Review (SLR) is introduced to identify HEVC challenges and potential research directions for interested researchers and developers. The time scope of this SLR covers all research articles published during the last six years starting from January 2014 up to the end of April 2020. Forty-two articles have met the criteria of selection out of 343 articles published in this area during the mentioned time scope. A new classification has been drawn followed by an identification of the challenges of implementing HEVC watermarking techniques based on the analysis and discussion of those chosen articles. Eventually, recommendations for HEVC watermarking techniques have been listed to help researchers to improve the existing techniques or to design new efficient ones.

The researcher has adopted a digital watermarking technique which operates in the frequency domain: a hybrid watermarking scheme based joint discrete wavelet transform -discrete cosine transform -(DWT-DCT). Its main objective is to test whether this technique can withstand attacks (its robustness) and invisibility (its imperceptibility), achieved by taking DCT of the DWT coefficients of the LL mid-frequency sub-bands from its band. To ensure security, the secret code (watermark) is scrambled using the Arnold transformation which is embedded in the original host image; only gray-scale digital images are used. The results of this research reveal that the secret code (watermark) is strong enough against threats (noise). Comparative results are measured using signal-tonoise ratio criterions, mean square error and normalized cross correlation. Simulated experimentation is done in Matlab.

The importance of recommendation systems is increasing day by day due to the massive number of data and information-overloaded arising from the internet. This data can be collected in predictive datasets; these datasets can be processed and analysed via data mining methods. In this paper, an efficient hybrid movie recommender system has been designed using the association rules mining technique and K-nearest neighbours (KNN) algorithm as a classification method. The K-nearest neighbours (KNN) algorithm subsystem was used to create the first candidate list through a practical MovieLens dataset, which was retrieved from the source of the NetFlix network. Beside, the Apriori algorithm subsystem is used to analyse the same dataset and create the second list. Finally, the proposed system creates a final recommended list by matching the two lists. The results of the proposed system provide better performance than the existing systems in terms of the important degree. The important degree gives a better accuracy rate than the existing techniques used.