Encryption of Motion Vector Based Compressed Video Data

Author(s):  
Manoj K Mishra ◽  
Susanta Mukhopadhyay ◽  
G. P. Biswas
2012 ◽  
Vol 532-533 ◽  
pp. 1219-1224
Author(s):  
Hong Tao Deng

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.


Author(s):  
Suvojit Acharjee ◽  
Sayan Chakraborty ◽  
Wahiba Ben Abdessalem Karaa ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey

Video is an important medium in terms of information sharing in this present era. The tremendous growth of video use can be seen in the traditional multimedia application as well as in many other applications like medical videos, surveillance video etc. Raw video data is usually large in size, which demands for video compression. In different video compressing schemes, motion vector is a very important step to remove the temporal redundancy. A frame is first divided into small blocks and then motion vector for each block is computed. The difference between two blocks is evaluated by different cost functions (i.e. mean absolute difference (MAD), mean square error (MSE) etc).In this paper the performance of different cost functions was evaluated and also the most suitable cost function for motion vector estimation was found.


2001 ◽  
Vol 47 (3) ◽  
pp. 319-325 ◽  
Author(s):  
Mei-Juan Chen ◽  
Ming-Chung Chu ◽  
Shen-Yi Lo

2016 ◽  
Vol 42 (1) ◽  
pp. 5-9
Author(s):  
Tariq Fadil

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results.


2017 ◽  
Vol 1 (1) ◽  
pp. 57
Author(s):  
Pietro Camarda ◽  
Cataldo Guaragnella ◽  
Domenico Striccoli

Compressed variable bit rate (VBR) video transmission is acquiring a growing importance in the telecommunication world. High data rate variability of compressed video over multiple time scales makes an efficient bandwidth resource utilization difficult to obtain. One of the approaches developed to face this problem are smoothing techniques. Various smoothing algorithms that exploit client buffers have been proposed, thus reducing the peak rate and highrate variability by efficiently scheduling the video data to be transmitted over the network. The novel smoothing algorithm proposed in this paper, which represents a significant improvements over the existing methods,performs data scheduling both for a single stream and for stream aggregations, by taking into account available bandwidth constraints. It modifies, whenever possible, the smoothing schedule in such a way as to eliminate frame losses due to available bandwidth limitations. This technique can be applied to any smoothing algorithm already present in literature and can be usefully exploited to minimize losses in multiplexed stream scenarios, like Terrestrial Digital Video Broadcasting (DVB-T), where a specific known available bandwidth must be shared byseveral multimedia flows. The developed algorithm has been exploited for smoothing stored video, although it can also be quite easily adapted for real time smoothing. The obtained numerical results, compared with the MVBA, another smoothing algorithm that is already presented and discussed in literature, show the effectiveness of the proposed algorithm, in terms of lost video frames, for different multiplexed scenarios.


2021 ◽  
Vol 2021 (1) ◽  
pp. 78-82
Author(s):  
Pak Hung Chan ◽  
Georgina Souvalioti ◽  
Anthony Huggett ◽  
Graham Kirsch ◽  
Valentina Donzella

Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.


2018 ◽  
Author(s):  
Vandana Rathore ◽  
Ashish Verma ◽  
Aashish Kumar ◽  
Arup Kumar Pal
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