Optimal Dynamic Rate Shaping for Compressed Video Streaming

Kim, Minjung; Altunbasak, Y.

doi:10.1007/3-540-47734-9_78

Cited by 4 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the bandwidth capacity predicted via monitoring the current state of the network, the video is dynamically reshaped by being encoded with different quantization values. Extending this idea, Kim and Altunbasak [10] suggested a technique to reshape video by scaling its spatial, temporal and SNR properties. This technique was later generalized into a utilitybased framework by Kim et al [9].…”

Section: Related Workmentioning

confidence: 99%

Critical video quality for distributed automated video surveillance

Korshunov

Ooi

2005

Proceedings of the 13th Annual ACM International Conference on Multimedia

View full text Add to dashboard Cite

Large-scale distributed video surveillance systems pose new scalability challenges. Due to the large number of video sources in such systems, the amount of bandwidth required to transmit video streams for monitoring often strains the capability of the network. On the other hand, large-scale surveillance systems often rely on computer vision algorithms to automate surveillance tasks. We observe that these surveillance tasks present an opportunity for trade-off between the accuracy of the tasks and the bit rate of the video being sent. This paper shows that there exists a sweet spot, which we term critical video quality that can be used to reduce video bit rate without significantly affecting the accuracy of the surveillance tasks. We demonstrate this point by running extensive experiments on standard face detection and face tracking algorithms. Our experiments show that face detection works equally well even if the quality of compression is significantly reduced, and face tracking still works even if the frame rate is reduced to 6 frames per second. We further develop a prototype video surveillance system to demonstrate this idea. Our evaluation shows that we can achieve up to 29 times reduction in video bit rate when detecting faces and 16 times reduction when tracking faces. This paper also proposes a formal rate-accuracy optimization framework which can be used to determine appropriate encoding parameters in distributed video surveillance systems that are subjected to either bandwidth constraints or accuracy constraints.

show abstract

Section: Related Workmentioning

confidence: 99%

Critical video quality for distributed automated video surveillance

Korshunov

Ooi

2005

Proceedings of the 13th Annual ACM International Conference on Multimedia

View full text Add to dashboard Cite

show abstract

“…With video rate adaptation, the videos are shaped or transformed to meet the bandwidth constraint [7,16,15]. At the receiver side, the video is decoded and rendered.…”

Section: Figure 2: Video Encoding Block Diagrammentioning

confidence: 99%

Aggregate power consumption modeling of live video streaming systems

Sharrab

Sarhan

2013

Proceedings of the 4th ACM Multimedia Systems Conference

View full text Add to dashboard Cite

Power consumption of video streaming systems has become a major concern, especially in battery-powered devices, such as video sensors. Power is usually dissipated in each one of the major phases of the streaming process: capturing, encoding, and transmission. This paper develops models for power consumption in each of these phases and validates them with extensive experiments, focusing primarily on H.264 video encoding. For comparative purposes, we also study MJPEG and MPEG-4 video codecs. In addition, we analyze the impacts of the main H.264 video compression parameters on power consumption and bitrate. These parameters include quantization parameter, number of reference frames, motion estimation (ME) range, and ME algorithm.

show abstract

“…Based on the bandwidth capacity predicted via monitoring the current state of the network, the video is dynamically reshaped with different quantization values. Extending this idea, Kim and Altunbasak [Kim and Altunbasak 2001] suggested a technique to reshape video by scaling its spatial, temporal, and SNR properties. This technique was later generalized into a utility-based framework by Kim et al [Kim et al 2003].…”

Section: Related Workmentioning

confidence: 99%

Video quality for face detection, recognition, and tracking

Korshunov

Ooi

2011

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

Many distributed multimedia applications rely on video analysis algorithms for automated video and image processing. Little is known, however, about the minimum video quality required to ensure an accurate performance of these algorithms. In an attempt to understand these requirements, we focus on a set of commonly used face analysis algorithms. Using standard datasets and live videos, we conducted experiments demonstrating that the algorithms show almost no decrease in accuracy until the input video is reduced to a certain critical quality, which amounts to significantly lower bitrate compared to the quality commonly acceptable for human vision. Since computer vision percepts video differently than human vision, existing video quality metrics, designed for human perception, cannot be used to reason about the effects of video quality reduction on accuracy of video analysis algorithms. We therefore investigate two alternate video quality metrics, blockiness and mutual information, and show how they can be used to estimate the critical video qualities for face analysis algorithms.

show abstract

Optimal Dynamic Rate Shaping for Compressed Video Streaming

Cited by 4 publications

References 7 publications

Critical video quality for distributed automated video surveillance

Critical video quality for distributed automated video surveillance

Aggregate power consumption modeling of live video streaming systems

Video quality for face detection, recognition, and tracking

Contact Info

Product

Resources

About