Energy-aware decoders: A case study based on an RVC-CAL specification

Ren, Rong; Juárez, E.; Sanz, C.; Raulet, Mickaël; Pescador, F.

doi:10.1109/dasip.2014.7115632

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…For the feature based models, we could even achieve a lower estimation error (3.63% in [18]) which is caused by the more sophisticated training method. In addition, reported errors for model PE correspond to our findings (more than three quarters of the tested sequences in [14] showed an estimation error between 5 − 10% where we found a mean error of ∼ 8%). In contrast, Raoufi et al reported estimation errors of less than 1% (models H2 and H2 T ), which is much better than our results.…”

Section: Discussionsupporting

confidence: 89%

“…The first reference model (short PE) is proposed by Ren et al [14]. It estimates the decoding energy using processor level information.…”

Section: B Energy Model Based On Processor Eventsmentioning

confidence: 99%

“…The models presented before were all established for the H.264/AVC (Advanced Video Coding) standard [13]. To estimate the decoding energy of HEVC decoders, Ren et al [14] selected the processor-level parameters instructions, cache misses, and hardware interrupts as a foundation and achieved average relative estimation errors of less than 10%. In a different approach, we proposed a model based on high-level bit stream features [15] such as bit rate, resolution, and number of frames.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Energy Consumption of the HEVC Decoding Process

Herglotz,

Springer,

Reichenbach

et al. 2022

Preprint

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In this paper, we present a bit stream feature based energy model that accurately estimates the energy required to decode a given HEVC-coded bit stream. Therefore, we take a model from literature and extend it by explicitly modeling the inloop filters, which was not done before. Furthermore, to prove its superior estimation performance, it is compared to seven different energy models from literature. By using a unified evaluation framework we show how accurately the required decoding energy for different decoding systems can be approximated. We give thorough explanations on the model parameters and explain how the model variables are derived. To show the modeling capabilities in general, we test the estimation performance for different decoding software and hardware solutions, where we find that the proposed model outperforms the models from literature by reaching frame-wise mean estimation errors of less than 7% for software and less than 15% for hardware based systems.

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Section: Discussionsupporting

confidence: 89%

“…The first reference model (short PE) is proposed by Ren et al [14]. It estimates the decoding energy using processor level information.…”

Section: B Energy Model Based On Processor Eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the Energy Consumption of the HEVC Decoding Process

Herglotz,

Springer,

Reichenbach

et al. 2022

Preprint

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show abstract

“…The third approach focuses on the type of the bit stream. In that regard, Ren et al [27] proposed switching between different coding standards. When the battery is low, the decoder can switch from HEVC decoding to H.264/AVC decoding and extend the operating time by more than 5%.…”

Section: Literature Reviewmentioning

confidence: 99%

Decoding-Energy-Rate-Distortion Optimization for Video Coding

Herglotz,

Heindel,

Kaup

2022

Preprint

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This paper presents a method for generating coded video bit streams requiring less decoding energy than conventionally coded bit streams. To this end, we propose extending the standard rate-distortion optimization approach to also consider the decoding energy. In the encoder, the decoding energy is estimated during runtime using a feature-based energy model. These energy estimates are then used to calculate decodingenergy-rate-distortion costs that are minimized by the encoder. This ultimately leads to optimal trade-offs between these three parameters. Therefore, we introduce the mathematical theory for describing decoding-energy-rate-distortion optimization and the proposed encoder algorithm is explained in detail. For rateenergy control, a new encoder parameter is introduced. Finally, measurements of the software decoding process for HEVC-coded bit streams are performed. Results show that this approach can lead to up to 30% of decoding energy reduction at a constant visual objective quality when accepting a bitrate increase at the same order of magnitude.

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“…Broadly, the state-of-the-art attempts to reduce decoding-complexity include efficient decoder implementations [5][6][7][8], dynamic alterations to the decoding process [9], dynamic voltage and frequency scaling (DVFS) methods [10][11][12][13], content adaptation based on scalable coding architectures [14][15][16], and media transcoding [17] approaches. Out of these, the latter two are encoder-side operations, and operate together with HTTP adaptive streaming solutions such as MPEG-DASH [2,18] to fetch videos of different decoding-complexities based on a device's remaining energy level.…”

Section: Introductionmentioning

confidence: 99%

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

et al. 2020

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Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.

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Energy-aware decoders: A case study based on an RVC-CAL specification

Cited by 5 publications

References 13 publications

Modeling the Energy Consumption of the HEVC Decoding Process

Modeling the Energy Consumption of the HEVC Decoding Process

Decoding-Energy-Rate-Distortion Optimization for Video Coding

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

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