PRIVACY INFORMATION PROTECTION IN AN ENCRYPTED COMPRESSED H.264 VIDEO BITSTREAM

2015 ◽  
Vol 3 (4) ◽  
pp. 343-345
Author(s):  
Pradeep Rajagopalan ◽  
Sanjay Kumar Gengaiyan
Keyword(s):  
Video Quality ◽  
Motion Vector ◽  
Video Stream ◽  
Security And Privacy ◽  
Original Video ◽  
Video Content ◽  
Compressed Video ◽  
High Data ◽  
Computation Efficiency ◽  
Wrapping Technique

The paper presents that encryption of compressed video bit streams and hiding privacy information to protect videos during transmission or cloud storage. Digital video sometimes needs to be stored and processed in an encrypted format to maintain security and privacy. Here, data hiding directly in the encrypted version of H.264/AVC video stream is approached, which includes the following three parts. By analyzing he property of H.264/AVC codec, the code words of intra prediction modes, the code words of motion vector differences, and the code words of residual coefficients are encrypted with stream ciphers. Then, a data hider may embed additional data in the encrypted domain by using wrapping technique, without knowing the original video content. The paper results shows that used methods provides better performance in terms of computation efficiency, high data security and video quality after decryption. The parameters such as RMSE, PSNR, CC are evaluated to measure its efficiency

An Improved Error Concealment Based on Redundant Motion Vector Information

2012 ◽  
Vol 532-533 ◽  
pp. 1219-1224
Author(s):  
Hong Tao Deng
Keyword(s):  
Video Transmission ◽  
Error Concealment ◽  
Video Quality ◽  
Motion Vector ◽  
Compressed Video ◽  
Vector Information ◽  
Vector Estimation ◽  
Channel Errors ◽  
Bit Stream

During video transmission over error prone network, compressed video bit-stream is sensitive to channel errors that may degrade the decoded pictures severely. In order to solve this problem, error concealment technique is a useful post-processing tool for recovering the lost information. In these methods, how to estimate the lost motion vector correctly is important for the quality of decoded picture. In order to recover the lost motion vector, an Decoder Motion Vector Estimation (DMVE) criterion was proposed and have well effect for recover the lost blocks. In this paper, we propose an improved error concealment method based on DMVE, which exploits the accurate motion vector by using redundant motion vector information. The experimental results with an H.264 codec show that our method improves both subjective and objective decoder reconstructed video quality, especially for sequences of drastic motion.

scholarly journals Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6429
Author(s):  
Liqun Lin ◽  
Jing Yang ◽  
Zheng Wang ◽  
Liping Zhou ◽  
Weiling Chen ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Video Quality ◽  
Video Stream ◽  
Support Vector ◽  
Compressed Video ◽  
Artifact Detection ◽  
Video Signals ◽  
Reference Information ◽  
Video Saliency ◽  
Video Quality Metric

Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.

A No-Reference Quality Assessment System for Video Streaming over RTP

2011 ◽  
Vol 179-180 ◽  
pp. 243-248
Author(s):  
Fu Zheng Yang ◽  
Jia Run Song ◽  
Shu Ai Wan
Keyword(s):  
Quality Assessment ◽  
Video Streaming ◽  
High Efficiency ◽  
Video Quality ◽  
Video Stream ◽  
Assessment System ◽  
Original Video ◽  
Reference Quality ◽  
Networked Video

In the paper a no-reference system for quality assessment of video streaming over RTP is proposed for monitoring the quality of networked video. The proposed system is composed of four modules, where the quality assessment module utilizes information extracted from the bit-stream by the modules of RTP header analysis, frame header analysis and display buffer simulation. Taking MPEG-4 encoded video stream over RTP as an example, the process of video quality assessment using the proposed system is described in this paper. The proposed system is featured by its high efficiency without sorting to the original video or video decoding, and therefore well suited for real-time networked video applications.

An Adaptive Intra-Frame Refresh Algorithm Based on Rate Distortion Optimization

2013 ◽  
Vol 346 ◽  
pp. 123-128
Author(s):  
Song Jian Bao
Keyword(s):  
Motion Vector ◽  
Main Idea ◽  
Selection Effect ◽  
Rate Distortion ◽  
Video Stream ◽  
Video Code ◽  
Compressed Video ◽  
Rate Distortion Optimization ◽  
Error Performance ◽  
Macro Block

The biggest drawback of compressed video code flow is very sensitive to the transmission error. Put forward the motion vector-adaptive intra refresh (MV-AIR) and the rate distortion optimization-adaptive intra refresh (RDO-AIR). The main idea of RDO-AIR is that using the rate-distortion function calculations expense determines whether a macro-block refreshed, it considers the rate distortion estimation for macro-block models selection effect, each frame is dynamically intra-frame refreshed. These two kinds of algorithm by considering the changes of the video itself content characteristics and channel error rate characteristics can be adaptively determine macro-blocks number and macro-block position. Simulation found that the two algorithms effectively enhanced the video stream resistance error performance, at the same time, and the both didnt significantly increase the output bit rate.

MENDETEKSI ORISINALITAS FILE VIDEO MENERAPKAN METODE MD5

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Ermi Suryani Nasution
Keyword(s):  
Video Quality ◽  
Video Recording ◽  
Multimedia Content ◽  
Original Video ◽  
Video Content ◽  
Video File ◽  
Message Digest ◽  
Video Manipulation

Video is one of the multimedia content. In the video the video is stored in a file that can be changed intentionally or unintentionally. To ensure video content does not change, a mechanism is needed to detect the integrity of the video content both from the video quality and the size of the video file. In this research, video manipulation with change in duration, addition of frame and change in video file extension aims to compare the original video recording and manipulation video recording. Based on Hasher Pro testing results show that the system is running well, successfully detecting changes that occur in video files scanning video files with a percentage of 100%, all changes can be detected by the Message Digest (MD5) algorithm on the hash value.Keywords: Video, Metode Massage Digest 5 (MD5), Hasher Pro.

Analyzing the Quality of Aesthetic Surgery Procedure Videos on TikTok

2021 ◽  
Author(s):  
Anjali Om ◽  
Bobby Ijeoma ◽  
Sara Kebede ◽  
Albert Losken
Keyword(s):  
Social Media ◽  
Breast Augmentation ◽  
Cosmetic Surgery ◽  
Video Quality ◽  
Aesthetic Surgery ◽  
Video Content ◽  
Medical Providers ◽  
Cosmetic Procedure ◽  
Different Content

Abstract Background TikTok is one of the most popular and fastest growing social media apps in the world. Previous studies have analyzed the quality of patient education information on older video platforms, but the quality of plastic and cosmetic surgery videos on TikTok has not yet been determined. Objectives To analyze the source and quality of certain cosmetic procedure videos on TikTok. Methods The TikTok mobile application was queried for content related to two popular face procedures (rhinoplasty and blepharoplasty) and two body procedures (breast augmentation and abdominoplasty). Two independent reviewers analyzed video content according to the DISCERN scale, a validated, objective criteria that assesses the quality of information on a scale of 1-5. Quality scores were compared between videos produced by medical and nonmedical creators and between different content categories. Results There were 4.8 billion views and 76.2 million likes across included videos. Videos were created by MDs (56%) and laypersons (44%). Overall average DISCERN score out of 5 corresponded to very poor video quality for rhinoplasty (1.55), blepharoplasty (1.44), breast augmentation (1.25) and abdominoplasty (1.29). DISCERN scores were significantly higher among videos produced by MDs than by laypersons for all surgeries. Comedy videos consistently had the lowest average DISCERN scores, while educational videos had the highest. Conclusions It is increasingly important that medical professionals understand the possibility of patient misinformation in the age of social media. We encourage medical providers to be involved in creating quality information on TikTok and educate patients about misinformation to best support health literacy.

Copy Detection Using Graphical Model

2012 ◽  
pp. 261-279
Author(s):  
Shikui Wei ◽  
Yao Zhao ◽  
Zhenfeng Zhu
Keyword(s):  
Graphical Model ◽  
Graph Model ◽  
Video Stream ◽  
Temporal Consistency ◽  
Video Content ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Video Sharing ◽  
Frame Fusion ◽  
Temporal Localization

With the growing popularity of video sharing websites and editing tools, it is easy for people to involve the video content from different sources into their own work, which raises the copyright problem. Content-based video copy detection attempts to track the usage of the copyright-protected video content by using video analysis techniques, which deals with not only whether a copy occurs in a query video stream but also where the copy is located and where the copy is originated from. While a lot of work has addressed the problem with good performance, less effort has been made to consider the copy detection problem in the case of a continuous query stream, for which precise temporal localization and some complex video transformations like frame insertion and video editing need to be handled. In this chapter, the authors attack the problem by employing the graphical model to facilitate the frame fusion based video copy detection approach. The key idea is to convert frame fusion problem into graph model decoding problem with the temporal consistency constraint and three relaxed constraints. This work employs the HMM model to perform frame fusion and propose a Viterbi-like algorithm to speedup frame fusion process.

Confidentiality and Safekeeping Problems and Techniques in Fog Computing

2021 ◽  
pp. 1855-1873
Author(s):  
Nida Kauser Khanum ◽  
Pankaj Lathar ◽  
G. M. Siddesh
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Fog Computing ◽  
Video Stream ◽  
Security And Privacy ◽  
Surveillance Video ◽  
Speech Data ◽  
Real Time Applications ◽  
Privacy Issues ◽  
Intrusion Detection And Prevention

Fog computing is an extension of cloud computing, and it is one of the most important architypes in the current world. Fog computing is like cloud computing as it provides data storage, computation, processing, and application services to end-users. In this chapter, the authors discuss the security and privacy issues concerned with fog computing. The issues present in cloud are also inherited by fog computing, but the same methods available for cloud computing are not applicable to fog computing due to its decentralized nature. The authors also discuss a few real-time applications like healthcare systems, intelligent food traceability, surveillance video stream processing, collection, and pre-processing of speech data. Finally, the concept of decoy technique and intrusion detection and prevention technique is covered.

scholarly journals Low delay error resilience algorithm for H.265|HEVC video transmission

2019 ◽  
Vol 17 (6) ◽  
pp. 2047-2063
Author(s):  
Taha T. Alfaqheri ◽  
Abdul Hamid Sadka
Keyword(s):  
Video Coding ◽  
Video Transmission ◽  
High Efficiency ◽  
Video Quality ◽  
Error Resilience ◽  
Video Codec ◽  
Compressed Video ◽  
High Efficiency Video Coding ◽  
Low Delay ◽  
The Impact

AbstractTransmission of high-resolution compressed video on unreliable transmission channels with time-varying characteristics such as wireless channels can adversely affect the decoded visual quality at the decoder side. This task becomes more challenging when the video codec computational complexity is an essential factor for low delay video transmission. High-efficiency video coding (H.265|HEVC) standard is the most recent video coding standard produced by ITU-T and ISO/IEC organisations. In this paper, a robust error resilience algorithm is proposed to reduce the impact of erroneous H.265|HEVC bitstream on the perceptual video quality at the decoder side. The proposed work takes into consideration the compatibility of the algorithm implementations with and without feedback channel update. The proposed work identifies and locates the frame’s most sensitive areas to errors and encodes them in intra mode. The intra-refresh map is generated at the encoder by utilising a grey projection method. The conducted experimental work includes testing the codec performance with the proposed work in error-free and error-prone conditions. The simulation results demonstrate that the proposed algorithm works effectively at high packet loss rates. These results come at the cost of a slight increase in the encoding bit rate overhead and computational processing time compared with the default HEVC HM16 reference software.

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