Frame-rate adaptation and bi-directional coding for video sequences

2002 ◽  
Author(s):  
B. Alexandre ◽  
N. Laurent
Keyword(s):  
Rate Adaptation ◽  
Frame Rate ◽  
Video Sequences ◽  
Directional Coding

scholarly journals HFR Projector Camera Based Visible Light Communication System for Real-Time Video Streaming

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5368
Author(s):  
Atul Sharma ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo ◽  
Idaku Ishii
Keyword(s):  
Visible Light ◽  
Real Time ◽  
Video Streaming ◽  
Video Sequence ◽  
Vision System ◽  
Visible Light Communication ◽  
Gray Code ◽  
Frame Rate ◽  
Video Sequences ◽  
Color Video

This study develops a projector–camera-based visible light communication (VLC) system for real-time broadband video streaming, in which a high frame rate (HFR) projector can encode and project a color input video sequence into binary image patterns modulated at thousands of frames per second and an HFR vision system can capture and decode these binary patterns into the input color video sequence with real-time video processing. For maximum utilization of the high-throughput transmission ability of the HFR projector, we introduce a projector–camera VLC protocol, wherein a multi-level color video sequence is binary-modulated with a gray code for encoding and decoding instead of pure-code-based binary modulation. Gray code encoding is introduced to address the ambiguity with mismatched pixel alignments along the gradients between the projector and vision system. Our proposed VLC system consists of an HFR projector, which can project 590 × 1060 binary images at 1041 fps via HDMI streaming and a monochrome HFR camera system, which can capture and process 12-bit 512 × 512 images in real time at 3125 fps; it can simultaneously decode and reconstruct 24-bit RGB video sequences at 31 fps, including an error correction process. The effectiveness of the proposed VLC system was verified via several experiments by streaming offline and live video sequences.

scholarly journals Sensing Human-Screen Interaction for Energy-Efficient Frame Rate Adaptation on Smartphones

2015 ◽  
Vol 14 (8) ◽  
pp. 1698-1711 ◽  
Author(s):  
Jiadi Yu ◽  
Haofu Han ◽  
Hongzi Zhu ◽  
Yingying Chen ◽  
Jie Yang ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Rate Adaptation ◽  
Frame Rate

scholarly journals A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2708 ◽  
Author(s):  
Jutamanee Auysakul ◽  
He Xu ◽  
Vishwanath Pooneeth
Keyword(s):  
Kalman Filter ◽  
Poor Performance ◽  
Video Data ◽  
Frame Rate ◽  
Video Sequences ◽  
Video Stabilization ◽  
Switching Function ◽  
Post Processing ◽  
The Poor ◽  
Hybrid Motion

Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade–Lucus–Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.

scholarly journals Machine-Type Video Communication Using Pretrained Network for Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ran Li ◽  
Peinan Hao ◽  
Fengyuan Sun ◽  
Yanling Li ◽  
Lei You
Keyword(s):  
Internet Of Things ◽  
Video Transmission ◽  
Frame Rate ◽  
Video Sequences ◽  
Video Communications ◽  
Machine Type ◽  
Video Frames ◽  
Communication Devices ◽  
Frame Rate Up Conversion ◽  
Original Frame

With the increasing demand for internet of things (IoT) applications, machine-type video communications have become an indispensable means of communication. It is changing the way we live and work. In machine-type video communications, the quality and delay of the video transmission should be guaranteed to satisfy the requirements of communication devices at the condition of limited resources. It is necessary to reduce the burden of transmitting video by losing frames at the video sender and then to increase the frame rate of transmitting video at the receiver. In this paper, based on the pretrained network, we proposed a frame rate up-conversion (FRUC) algorithm to guarantee low-latency video transmitting in machine-type video communications. At the IoT node, by periodically discarding the video frames, the video sequences are significantly compressed. At the IoT cloud, a pretrained network is used to extract the feature layers of the transmitted video frames, which is fused into the bidirectional matching to produce the motion vectors (MVs) of the losing frames, and according to the output MVs, the motion-compensated interpolation is implemented to recover the original frame rate of the video sequence. Experimental results show that the proposed FRUC algorithm effectively improve both objective and subjective qualities of the transmitted video sequences.

scholarly journals Data Reduction and Frame Rate Adaptation in WVSN

2021 ◽  
Author(s):  
Jana Koteich ◽  
Christian Salim ◽  
Nathalie Mitton
Keyword(s):  
Data Reduction ◽  
Rate Adaptation ◽  
Frame Rate
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