A Perceptual Encryption Scheme for HEVC Video with Lossless Compression

2018 ◽  
Vol 10 (1) ◽  
pp. 67-78
Author(s):  
Juan Chen ◽  
Fei Peng
Keyword(s):  
High Efficiency ◽  
Motion Vector ◽  
Computational Cost ◽  
Video On Demand ◽  
Encryption Scheme ◽  
Video Content ◽  
High Efficiency Video Coding ◽  
Vector Difference ◽  
Perceptual Encryption ◽  
Low Computational Cost

Aiming to protect the video content and facilitate online video consumption, a perceptual encryption scheme is proposed for high efficiency video coding (HEVC) video. Based on RC4 algorithm, a key stream generation method is constructed, whose proportion of “1” and “0” can be regulated. During HEVC encoding, four kinds of syntax elements including motion vector difference (MVD)' sign, MVD's amplitude, sign of the luma residual coefficient and sign of the chroma residual coefficient, are encrypted by the regulated key stream. Experimental results and analysis show that the proposed scheme has good perceptual protection for the video content, and some advantages such as low computational cost, format-compliance and no bitrate increase can be achieved. It provides an effective resolution for the paid video-on-demand services.

A Format-Compliant Encryption for Secure HEVC Video Sharing in Multimedia Social Network

2018 ◽  
Vol 10 (2) ◽  
pp. 23-39
Author(s):  
Min Long ◽  
Fei Peng ◽  
Xiaoqing Gong
Keyword(s):  
Social Network ◽  
High Efficiency ◽  
Motion Vector ◽  
Control Factor ◽  
Encryption Scheme ◽  
High Efficiency Video Coding ◽  
Video Sharing ◽  
Nonzero Coefficient ◽  
Vector Difference ◽  
Multimedia Social Network

Aiming at secure video sharing in multimedia social network, a format-compliant encryption scheme for high efficiency video coding (HEVC) based on sigh data hiding (SDH) is proposed. The encryption is tightly integrated with the encoding/decoding processes. For each coding unit (CU), the sign of the nonzero coefficient and the first hiding nonzero coefficient are both encrypted with key stream. Meanwhile, one of merging index, motion vector prediction index, sign of motion vector difference and reference frame index is chosen for encryption according to a control factor. As it is explored in this article, experimental results and analysis indicate that it can effectively resist brute-force attack, difference attack and replacement attack. Also, it can keep a good balance in encryption space, computation complexity and security. Based on the encryption scheme, a framework of its implementation in multimedia social network is presented. It has great potential to be implemented for secure video sharing in multimedia social network.

A selective encryption scheme of CABAC based on video context in high efficiency video coding

2017 ◽  
Vol 77 (10) ◽  
pp. 12837-12851 ◽  
Author(s):  
Jianjun Li ◽  
Chenyan Wang ◽  
Xie Chen ◽  
Zheng Tang ◽  
Guobao Hui ◽  
...  
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Encryption Scheme ◽  
Selective Encryption ◽  
High Efficiency Video Coding

scholarly journals Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

2021 ◽  
Author(s):  
Rizwan Qureshi ◽  
Mehmood Nawaz
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Motion Vector ◽  
Super Resolution ◽  
Computational Time ◽  
Video Frame ◽  
Low Resolution ◽  
High Efficiency Video Coding ◽  
High Definition ◽  
Resolution Method

Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.

scholarly journals Design and ARM-Based Implementation of Bitstream-Oriented Chaotic Encryption Scheme for H.264/AVC Video

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1431
Author(s):  
Zirui Zhang ◽  
Ping Chen ◽  
Weijun Li ◽  
Xiaoming Xiong ◽  
Qianxue Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Stream Cipher ◽  
High Efficiency ◽  
Motion Vector ◽  
Divide And Conquer ◽  
Chaotic Encryption ◽  
The Real ◽  
Actual Application ◽  
Time Motion ◽  
Vector Difference

In actual application scenarios of the real-time video confidential communication, encrypted videos must meet three performance indicators: security, real-time, and format compatibility. To satisfy these requirements, an improved bitstream-oriented encryption (BOE) method based chaotic encryption for H.264/AVC video is proposed. Meanwhile, an ARM-embedded remote real-time video confidential communication system is built for experimental verification in this paper. Firstly, a 4-D self-synchronous chaotic stream cipher algorithm with cosine anti-controllers (4-D SCSCA-CAC) is designed to enhance the security. The algorithm solves the security loopholes of existing self-synchronous chaotic stream cipher algorithms applied to the actual video confidential communication, which can effectively resist the combinational effect of the chosen-ciphertext attack and the divide-and-conquer attack. Secondly, syntax elements of the H.264 bitstream are analyzed in real-time. Motion vector difference (MVD) coefficients and direct-current (DC) components in Residual syntax element are extracted through the Exponential-Golomb decoding operation and entropy decoding operation based on the context-based adaptive variable length coding (CAVLC) mode, respectively. Thirdly, the DC components and MVD coefficients are encrypted by the 4-D SCSCA-CAC, and the encrypted syntax elements are re-encoded to replace the syntax elements of the original H.264 bitstream, keeping the format compatibility. Besides, hardware codecs and multi-core multi-threading technology are employed to improve the real-time performance of the hardware system. Finally, experimental results show that the proposed scheme, with the advantage of high efficiency and flexibility, can fulfill the requirement of security, real-time, and format compatibility simultaneously.

scholarly journals Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

2021 ◽  
Author(s):  
Rizwan Qureshi ◽  
Mehmood Nawaz
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Motion Vector ◽  
Super Resolution ◽  
Computational Time ◽  
Video Frame ◽  
Low Resolution ◽  
High Efficiency Video Coding ◽  
High Definition ◽  
Resolution Method

Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.

scholarly journals Performance Overview of the Latest Video Coding Proposals: HEVC, JEM and VVC

2021 ◽  
Vol 7 (2) ◽  
pp. 39
Author(s):  
Miguel O. Martínez-Rach ◽  
Héctor Migallón ◽  
Otoniel López-Granado ◽  
Vicente Galiano ◽  
Manuel P. Malumbres
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
State Of The Art ◽  
Rate Distortion ◽  
Video Content ◽  
Hardware Complexity ◽  
High Efficiency Video Coding ◽  
High Definition ◽  
The Cost ◽  
Performance Results

The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity.

Amazons Kampf um das Wohnzimmer

2016 ◽  
Vol 13 (3) ◽  
pp. 37-39
Author(s):  
Ralf Kaumanns
Keyword(s):  
Streaming Media ◽  
Video On Demand ◽  
Video Content ◽  
On Demand

Der Kampf um das Wohnzimmer ist voll entbrannt. Eine Reihe von Anbietern versuchen Streaming Media-Dienste im deutschen Markt zu etablieren. Amazon hat sich mit seiner Strategie eine marktführende Rolle erarbeiten können. Laut einer Analyse von Goldmedia¹ besitzt Amazon mittlerweile einen Anteil im Video-On-Demand-Markt von 38,9%, deutlich vor Wettbewerbern wie Apple, Maxdome, Google oder Netflix. Der Erfolg kommt nicht von ungefähr. Der Grund liegt vor allem in einer umfassenden Strategie rund um das Thema Bewegtbild und Video Content. Im Kampf um das Wohnzimmer haben selbst große und finanzkräftige Wettbewerber einen schweren Stand, um mit umfassend gebündelten Angeboten Schritt zu halten.

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