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Calculation of average PSNR differences between RD-curves
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The article was published on 2001-01-01 and is currently open access. It has received 4379 citations till now.read more
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Journal ArticleDOI
Efficient HEVC selective stream encryption using chaotic logistic map
TL;DR: Experimental results demonstrate the main feature of the proposed CLM-based HEVC SE, which turned out to save the time of the video encoding with remaining of the near visual distortion of the encrypted video stream by Glenn HEVCSE, which uses the Advanced Encryption Standard (AES).
Journal ArticleDOI
An MPEG-2 to H.264 Video Transcoder in the Baseline Profile
Gerardo Fernández-Escribano,Hari Kalva,José Luis Martínez,Pedro Cuenca,Luis Orozco-Barbosa,Antonio Garrido +5 more
TL;DR: This letter introduces in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain that outperforms the MB mode selection of the rate-distortion optimization option of the H. 264 encoder process by reducing the computational requirements by up to 90%, while maintaining the same coding efficiency.
Journal ArticleDOI
Adaptive Quantization Parameter Selection For H.265/HEVC by Employing Inter-Frame Dependency
TL;DR: An adaptive frame-level QP selection algorithm is proposed for the H.265/HEVC random access coding by taking into account the inter-frame dependency, and results show that in comparison with HM-16.0, the proposed algorithm reduces the BD-rate by 3.49% with negligible increase of encoding time.
Proceedings ArticleDOI
Fast wedgelet pattern decision for DMM in 3D-HEVC
TL;DR: An efficient algorithm of wedgelet pattern decision is proposed for the explicit wedgelet signaling or equivalently DMM1, which roughly estimates the position of sharp edges by pre-defined regions and only searches in the most probable region.
Proceedings ArticleDOI
Fast CU partition strategy for HEVC intra-frame coding using learning approach via random forests
TL;DR: This work proposes using the forest classifier to skip or terminate the current CU depth level, based on off-line training, and uses a machine learning technique: the random forests, for training to alleviate the intra encoding complexity.