Nowadays, a large part of the global energy consumption caused by video communications can be attributed to end-user devices such as smartphones, tablet PCs, and TV sets. In this paper, we present a method to increase the performance of an existing algorithm dedicated to reduce the end-user side energy consumption during video streaming. The algorithm, which is called decoding-energy-rate-distortion optimization (DERDO), exploits a decoding energy model during encoding and chooses coding modes in such a way that the software decoding energy is minimized. In this paper, we develop a dedicated gradient descent approach for DERDO that refines specific energy coefficients used for decoding energy modeling. We find that this approach boosts the performance of DERDO by increasing the energy savings by at least 5% with respect to standard DERDO. As a consequence, we observe decoding energy savings of more than 40% and more than 7% for practical encoder and decoder implementations of HEVC and H.264/AVC, respectively, when compared to standard encoding using classic rate-distortion optimization.

This paper discusses the new complexity metrics for VVC decoder power reduction introduced in the 3rdedition of the Green Metadata standard. The standard defines dedicated syntax elements that represent the expected software decoding complexity with a high accuracy. Using a simple complexity model, which can be trained for any VVC decoder software implementation, the receiver of the video can estimate the processing complexity to decode the subsequent video segment. Afterwards, it can adjust the clock frequency of the processor to keep the real-time playback constraint. By reducing the frequency of the processor, a significant amount of energy is saved. In this paper, we present the syntax elements, their meaning, and show that they can accurately estimate the processing complexity of various software decoder implementations with errors below 11%. Furthermore, we present a processor-frequency-control algorithm and apply it to a development board performing VVC video decoding. Measurements reveal that the complexity metric signaling can lead to up to 30% of energy savings.

Energy and compression efficiency are two essential parts of modern video decoder implementations that have to be considered. This work comprehensively studies the following six video coding formats regarding compression and decoding energy efficiency: AVC, VP9, HEVC, AV1, VVC, and AVM. We first evaluate the energy demand of reference and optimized software decoder implementations. Furthermore, we consider the influence of the usage of SIMD instructions on those decoder implementations. We find that AV1 is a sweet spot for optimized software decoder implementations with an additional energy demand of 16.55% and bitrate savings of -43.95% compared to VP9. We furthermore evaluate the hardware decoding energy demand of four video coding formats. Thereby, we show that AV1 has energy demand increases by 117.50% compared to VP9. For HEVC, we found a sweet spot in terms of energy demand with an increase of 6.06% with respect to VP9. Relative to their optimized software counterparts, hardware video decoders reduce the energy consumption to less than 9% compared to software decoders.

In this paper, we provide an in-depth assessment on the Bjøntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics including PSNR, VMAF, bitrate, and processing energies. These metrics are evaluated for visual data types such as classic perspective video, 360° video, point clouds, and screen content. As compression technology, we consider multiple hybrid video codecs as well as state-of-the-art neural network based compression methods. Using additional supporting points in-between standard points defined by parameters such as the quantization parameter, we assess the interpolation error of the Bjøntegaard-Delta (BD) calculus and its impact on the final BD value. From the analysis, we find that the BD calculus is most accurate in the standard application of rate-distortion comparisons with mean errors below 0.5 percentage points. For other applications and special cases, e.g., VMAF quality, energy considerations, or inter-codec comparisons, the errors are higher (up to 5 percentage points), but can be halved by using a higher number of supporting points. We finally come up with recommendations on how to use the BD calculus such that the validity of the resulting BD-values is maximized. Main recommendations are as follows: First, relative curve differences should be plotted and analyzed. Second, the logarithmic domain should be used for saturating metrics such as SSIM and VMAF. Third, BD values below a certain threshold indicated by the subset error should not be used to draw recommendations. Fourth, using two supporting points is sufficient to obtain rough performance estimates.

For 20 years, the gold standard to evaluate the performance of video codecs is to calculate average differences between rate-distortion 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%.

The optimization of the energy demand is crucial for modern video codecs. Previous studies show that the energy demand of VVC decoders can be improved by more than 50% if specific coding tools are disabled in the encoder. However, those approaches increase the bit rate by over 20% if the concept is applied to practical encoder implementations such as VVenC. Therefore, in this work, we investigate VVenC and study possibilities to reduce the additional bit rate, while still achieving low-energy decoding at reasonable encoding times. We show that encoding using our proposed coding tool profiles, the decoding energy efficiency is improved by over 25% with a bit rate increase of less than 5% with respect to standard encoding. Furthermore, we propose a second coding tool profile targeting maximum energy savings, which achieves 34% of energy savings at bitrate increases below 15%.

In recent studies, it could be shown that the energy demand of Versatile Video Coding (VVC) decoders can be twice as high as comparable High Efficiency Video Coding (HEVC) decoders. A significant part of this increase in complexity is attributed to the usage of new coding tools. By using a design space exploration algorithm, it was shown that the energy demand of VVC-coded sequences could be reduced if different coding tool profiles were used for the encoding process. This work extends the algorithm with several optimization strategies, methodological adjustments to optimize perceptual quality, and a new minimization criterion. As a result, we significantly improve the Pareto front, and the rate-distortion and energy efficiency of the state-of-the-art design space exploration. Therefore, we show an energy demand reduction of up to 47% with less than 30% additional bit rate, or a reduction of over 35% with approximately 6% additional bit rate.

In the past decade, we have witnessed an enormous growth in the demand for online video services. Recent studies estimate that nowadays, more than 1% of the global greenhouse gas emissions can be attributed to the production and use of devices performing online video tasks. As such, research on the true power consumption of devices and their energy efficiency during video streaming is highly important for a sustainable use of this technology. At the same time, over-the-top providers strive to offer high-quality streaming experiences to satisfy user expectations. Here, energy consumption and QoE partly depend on the same system parameters. Hence, a joint view is needed for their evaluation. In this paper, we perform a first analysis of both end-user power efficiency and Quality of Experience of a video streaming service. We take a crowdsourced dataset comprising 447,000 streaming events from YouTube and estimate both the power consumption and perceived quality. The power consumption is modeled based on previous work which we extended towards predicting the power usage of different devices and codecs. The user-perceived QoE is estimated using a standardized model. Our results indicate that an intelligent choice of streaming parameters can optimize both the QoE and the power efficiency of the end user device. Further, the paper discusses limitations of the approach and identifies directions for future research.

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.

In previous research, it was shown that the software decoding energy demand of High Efficiency Video Coding (HEVC) can be reduced by 15% by using a decoding-energy-ratedistortion optimization algorithm. To achieve this, the energy demand of the decoder has to be modeled by a bit stream feature-based model with sufficiently high accuracy. Therefore, we propose two bit stream feature-based models for the upcoming Versatile Video Coding (VVC) standard. The newly introduced models are compared with models from literature, which are used for HEVC. An evaluation of the proposed models reveals that the mean estimation error is similar to the results of the literature and yields an estimation error of 1.85% with 10-fold cross-validation.

This paper investigates the decoding energy and decoding time demand of VTM-7.0 in relation to HM-16.20. We present the first detailed comparison of two video codecs in terms of software decoder energy consumption. The evaluation shows that the energy demand of the VTM decoder is increased significantly compared to HM and that the increase depends on the coding configuration. For the coding configuration randomaccess, we find that the decoding energy is increased by over 80% at a decoding time increase of over 70%. Furthermore, results indicate that the energy demand increases by up to 207% when Single Instruction Multiple Data (SIMD) instructions are disabled, which corresponds to the HM implementation style. By measurements, it is revealed that the coding tools MIP, AMVR, TPM, LFNST, and MTS increase the energy efficiency of the decoder. Furthermore, we propose a new coding configuration based on our analysis, which reduces the energy demand of the VTM decoder by over 17% on average.

In previous research, it is shown that the decoding energy demand of several video codecs can be estimated accurately by using bit stream feature-based models. Therefore, we show in this paper that the visualization with the Decoding Energy Estimation Tool (DENESTO) can help to improve the understanding of the energy demand of the decoder.

Research has shown that decoder energy models are helpful tools for improving the energy efficiency in video playback applications. For example, an accurate feature-based bit stream model can reduce the energy consumption of the decoding process. However, until now only sequences of the SDR video format were investigated. Therefore, this paper shows that the decoding energy of HEVC-coded bit streams can be estimated precisely for different video formats and coding bit depths. Therefore, we compare a state-of-the-art model from the literature with a proposed model. We show that bit streams of the 360°, HDR, and fisheye video format can be estimated with a mean estimation error lower than 3.88% if the setups have the same coding bit depth. Furthermore, it is shown that on average, the energy demand for the decoding of bit streams with a bit depth of 10-bit is 55% higher than with 8-bit.

This paper shows that the processing energy of the decoder software for the next generation video codec can be accurately estimated using a feature based model. Therefore, a model from the literature is taken and extended to account for a high amount of the newly introduced coding modes. It is shown that using a selected set of 60 features, for a large set of more than 800 coded bit streams, a mean estimation error below 5% can be reached. Using the trained parameters of the model, the energy consumption of the decoder can be analyzed in detail such that, e.g., the coding modes consuming most processing energy can be identified. The model can be used inside the encoder for decoding- energy-rate-distortion optimization to generate decoding energy saving bit streams.

In this paper we show that a small amount of bit stream features can be used to accurately estimate the energy consumption of state-of-the-art software and hardware accelerated decoder implementations for four different video codecs. By testing the estimation performance on HEVC, H.264, H.263, and VP9 we show that the proposed model can be used for any hybrid video codec. We test our approach on a high amount of different test sequences to prove the general validity. We show that less than 20 features are sufficient to obtain mean estimation errors that are smaller than 8%. Finally, an example will show the performance trade-offs in terms of rate, distortion, and decoding energy for all tested codecs.