Implementing real-time video decoding on multimedia processors by complexity prediction techniques

Mattavelli, Marco; Brunetton, S.

doi:10.1109/30.713192

Cited by 32 publications

(12 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mattavelli et al [15] have shown that multimedia framedecoding rates cannot be modeled by any simple distribution. In order to further evaluate the performance of the two approaches, we use different random distributions to create our synthetic workloads and divide them into 6 groups:…”

Section: Evaluation Using Synthetic Workloadsmentioning

confidence: 99%

Control-theoretic dynamic frequency and voltage scaling for multimedia workloads

Hein

Humphrey

et al. 2002

Proceedings of the International Conference on Compilers, Architecture, and Synthesis for Embedded Systems - CASES '02

View full text Add to dashboard Cite

show abstract

Section: Evaluation Using Synthetic Workloadsmentioning

confidence: 99%

Control-theoretic dynamic frequency and voltage scaling for multimedia workloads

Hein

Humphrey

et al. 2002

Proceedings of the International Conference on Compilers, Architecture, and Synthesis for Embedded Systems - CASES '02

View full text Add to dashboard Cite

show abstract

“…Using a generic model of the frame decoding complexity eliminates this drawback. Such a complexity model for MPEG4 (seven parameters) is presented in [36]. They report accurate results (error 5%).…”

Section: B Aet Estimationmentioning

confidence: 90%

Application-directed voltage scaling

Pouwelse

Langendoen

Sips

2003

IEEE Trans. VLSI Syst.

View full text Add to dashboard Cite

Abstract-Clock (and voltage) scheduling is an important technique to reduce the energy consumption of processors that support voltage scaling. It is difficult, however, to achieve good results using only statistics from the operating system level when applications show bursty (unpredictable) behavior. We take the approach that such applications must be made power-aware and specify their average execution time (AET) and the deadline to the scheduler controlling the clock speed and processor voltage. This paper describes our energy priority scheduling (EPS) algorithm supporting power-aware applications. EPS orders tasks according to how tight their deadlines are and how often tasks overlap. Low-priority tasks are scheduled first, since they can be easily preempted to accommodate for high-priority tasks later. The EPS algorithm does not always yield the optimal schedule, but has a low complexity. We have implemented EPS on a StrongARM-based variable-voltage platform. We conducted experiments with a modified video decoder that estimates the AET of each frame. Measurements show that application-directed voltage scaling reduces processor power consumption with 50% for the bursty video decoder without missing any frame deadlines.Index Terms-Adaptive software, low power, power awareness, voltage scaling.

show abstract

“…The macroblock profiling that we consider as input to the scenario identification at the content provider side is similar to the platformindependent characterization done by Horowitz et al However, they do not study nor exploit similarities within and across video streams. Mattavelli and Brunetton [1998] also characterize video streams in a platformindependent manner by counting the number of inter prediction macroblocks, the number of intra prediction macroblocks, the number of skip macroblocks, etc. For predicting the decoder performance on a particular platform using these platformindependent metrics, they then employ linear regression to learn how to weight the various platform-independent metrics for making accurate performance predictions.…”

Section: Video Decoder Complexity and Resource Predictionmentioning

confidence: 99%

Exploiting media stream similarity for energy-efficient decoding and resource prediction

Hamers

Eeckhout

2012

ACM Trans. Embed. Comput. Syst.

View full text Add to dashboard Cite

This article introduces a novel approach to energy-efficient media stream decoding that is based on the notion of media stream similarity. The key idea is that platform-independent scenarios with similar decoding complexity can be identified within and across media streams. A device that decodes a media stream annotated with scenario information can then adjust its processor clock frequency and voltage level based on these scenarios for lower energy consumption. Our evaluation, done using the H.264 AVC decoder and 12 reference video streams, shows an average energy reduction of 44% while missing less than 0.2% of the frame deadlines using scenario-driven video decoding.An additional application of scenario-based media stream annotation is to predict required resources (compute power and energy) for consuming a given service on a given device. Resource prediction is extremely useful in a client-server setup in which the client requests a media service from the server or content provider. The content provider (in cooperation with the client) can then determine what service quality to deliver, given the client's available resources. Scenario-aware resource prediction can predict (compute power and energy) consumption with errors less than 4% (and an overall average 1.4% error).

show abstract

Implementing real-time video decoding on multimedia processors by complexity prediction techniques

Cited by 32 publications

References 1 publication

Control-theoretic dynamic frequency and voltage scaling for multimedia workloads

Control-theoretic dynamic frequency and voltage scaling for multimedia workloads

Application-directed voltage scaling

Exploiting media stream similarity for energy-efficient decoding and resource prediction

Contact Info

Product

Resources

About