Estimation of the optimal frame rate for video communications under bit-rate constraints

2003 ◽  
Vol 86 (12) ◽  
pp. 54-67 ◽  
Author(s):  
Yasuhiro Inazumi ◽  
Toshiyuki Yoshida ◽  
Yoshinori Sakai ◽  
Horita Yuukou
Keyword(s):  
Frame Rate ◽  
Video Communications ◽  
Bit Rate ◽  
Rate Constraints

Very Low Bit-rate Video Coding

2011 ◽  
pp. 100-148
Author(s):  
Manoranjan Paul ◽  
Manzur Murshed ◽  
Laurence S. Dooley
Keyword(s):  
Video Coding ◽  
Vector Quantization ◽  
Parametric Model ◽  
Future Research ◽  
The Internet ◽  
Discrete Wavelet ◽  
Video Communications ◽  
Bit Rate ◽  
Low Bit Rate ◽  
Shaped Pattern

his chapter presents a contemporary review of the various different strategies available to facilitate Very Low Bit-Rate (VLBR) coding for video communications over mobile and fixed transmission channels as well as the Internet. VLBR media is typically classified as having a bit rate between 8 and 64 Kbps. Techniques that are analyzed include Vector Quantization, various parametric model-based representations, the Discrete Wavelet and Cosine Transforms, and fixed and arbitrary shaped pattern-based coding. In addition to discussing the underlying theoretical principles and relevant features of each approach, the chapter also examines their benefits and disadvantages, together with some of the major challenges that remain to be solved. The chapter concludes by providing some judgments on the likely focus of future research in the VLBR coding field.

scholarly journals Optimal Stochastic Signaling Under Average Power and Bit Rate Constraints

2018 ◽  
Vol 66 (12) ◽  
pp. 6028-6039
Author(s):  
Cagri Goken ◽  
Berkan Dulek ◽  
Sinan Gezici
Keyword(s):  
Average Power ◽  
Bit Rate ◽  
Rate Constraints

scholarly journals H.264 SVC Complexity Reduction Based on Likelihood Mode Decision

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
L. Balaji ◽  
K. K. Thyagharajan
Keyword(s):  
Computational Complexity ◽  
Video Coding ◽  
Mode Selection ◽  
Mode Decision ◽  
Frame Rate ◽  
Fast Mode ◽  
Bit Rate ◽  
Time Saving ◽  
Decision Algorithm ◽  
Fast Mode Decision

H.264 Advanced Video Coding (AVC) was prolonged to Scalable Video Coding (SVC). SVC executes in different electronics gadgets such as personal computer, HDTV, SDTV, IPTV, and full-HDTV in which user demands various scaling of the same content. The various scaling is resolution, frame rate, quality, heterogeneous networks, bandwidth, and so forth. Scaling consumes more encoding time and computational complexity during mode selection. In this paper, to reduce encoding time and computational complexity, a fast mode decision algorithm based on likelihood mode decision (LMD) is proposed. LMD is evaluated in both temporal and spatial scaling. From the results, we conclude that LMD performs well, when compared to the previous fast mode decision algorithms. The comparison parameters are time, PSNR, and bit rate. LMD achieve time saving of 66.65% with 0.05% detriment in PSNR and 0.17% increment in bit rate compared with the full search method.

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.

Low Bit-Rate Image Coding for Facial Movement

1997 ◽  
Vol 07 (04) ◽  
pp. 249-259
Author(s):  
Kunio Takaya ◽  
R. Todd Reinhardt
Keyword(s):  
Image Coding ◽  
Image Data ◽  
Image Transmission ◽  
Mapping Method ◽  
Frame Rate ◽  
Video Frame ◽  
Bit Rate ◽  
Facial Image ◽  
Bilinear Mapping ◽  
Low Bit Rate

A method of low bit-rate facial image coding specifically designed for the use in video telephone is presented. The basic principle for this facial image coding is to exploit the capabilities of 2D image warping techniques to generate the replica of the sender's facial expression merely by deforming a master face image, which is sent once at the beginning of the telephone call. Several parameters that describe facial expressions are monitored at the transmitter at the video frame rate of 30 frames/s, and then transmitted to the receiver using the in-band data channel. Since transmission of actual image data happens once or when another image is required, the bit-rate is much lower that that required by ordinary video image transmission. A fast bilinear mapping method for warped images, a grid mesh set over the facial image, various warping algorithms to realize head movement and facial motions with respect to eyes and mouth are discussed. This paper also compares the presented low bit-rate facial image coding to its competing methods such as MPEG and the method using a 3D facial model.

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