Decoding-Energy-Rate-Distortion Optimization for Video Coding

Herglotz, Christian; Heindel, Andreas; Kaup, André

doi:10.1109/tcsvt.2017.2771819

Cited by 33 publications

(24 citation statements)

References 27 publications

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“…In future work, we will study if the energy efficiency of the proposed profiles can be enhanced by DERDO [14]. Also, block partitioning will be studied that might have further potential to reduce the decoder complexity.…”

Section: Discussionmentioning

confidence: 99%

“…This is achieved by changing the rate-distortion optimization (RDO) in the encoder. Therefore, RDO has to be adjusted to the energy domain [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the energy demand of the decoder is estimated in advance in the encoder with a bit stream feature-based energy model [15], which is called Decoding-Energy-Rate-Distortion-Optimization (DERDO) [14]. The authors achieve an energy reduction of up to 30 % for a maximum bit rate increase of 50 %.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Efficient Video Decoding for VVC Using a Greedy Strategy-Based Design Space Exploration

Kränzler

Herglotz

Kaup

2022

IEEE Trans. Circuits Syst. Video Technol.

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IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1 % of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50 % for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rate.

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Section: Discussionmentioning

confidence: 99%

“…This is achieved by changing the rate-distortion optimization (RDO) in the encoder. Therefore, RDO has to be adjusted to the energy domain [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficient Video Decoding for VVC Using a Greedy Strategy-Based Design Space Exploration

Kränzler

Herglotz

Kaup

2022

IEEE Trans. Circuits Syst. Video Technol.

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“…Furthermore, in contrast to replacing the quality metric, it was also proposed to replace the bitrate by other metrics describing technically limiting factors, for example the decoding energy or the decoding time [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Beyond Bjøntegaard: Limits of Video Compression Performance Comparisons

Herglotz¹,

Kränzler²,

Mons³

et al. 2022

Preprint

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For 20 years, the gold standard to evaluate the performance of video codecs is to calculate average differences between ratedistortion curves, also called the "Bjøntegaard Delta". With the help of this tool, the compression performance of codecs can be compared. In the past years, we could observe that the calculus was also deployed for other metrics than bitrate and distortion in terms of peak signal-to-noise ratio, for example other quality metrics such as video multi-method assessment fusion or hardware-dependent metrics such as the decoding energy. However, it is unclear whether the Bjøntegaard Delta is a valid way to evaluate these metrics. To this end, this paper reviews several interpolation methods and evaluates their accuracy using different performance metrics. As a result, we propose to use a novel approach based on Akima interpolation, which returns the most accurate results for a large variety of performance metrics. The approximation accuracy of this new method is determined to be below a bound of 1.5%.

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“…Action localization in videos is one of the most challenging problems in video understanding (Zhang and Ma, 2014;Chen et al, 2017;Khelifi and Bouridane, 2017;Ma et al, 2017;Herglotz et al, 2017;Yuan et al, 2017;Shiau et al, 2017;Afonso et al, 2019), which aims to classify the category of actions and localize actions in both time and space. In other words, given a video, we should not only recognize the category of actions (Peng et al, 2018;Zhao and Peng, 2017) but also localize the start time and end time of actions and further detect the corresponding bounding-boxes of subjects (actors).…”

Section: Introductionmentioning

confidence: 99%