An Empirical Study of Reducing AV1 Decoder Complexity and Energy Consumption via Encoder Parameter Tuning

2025-10-14

AI Summary

AV1’s high decoding complexity severely hinders its deployment on battery-constrained devices such as mobile terminals. To address this, we systematically quantify the impact of individual AV1 coding tools on decoding computational cost and energy consumption, and propose a lightweight, decoder-transparent energy-saving strategy—achieved solely by tuning encoder parameters. Using libaom-AV1 and SVT-AV1, we conduct precise cycle- and energy-level measurements on Intel platforms via RAPL and SoC Watch. Results show that disabling the CDEF filter reduces decoding cycles by 10%; further enabling SVT-AV1’s `fast-decode=2` preset yields an additional 24% cycle reduction, with BD-rate degradation under 0.5% and negligible perceptual quality loss. This work establishes, for the first time, an interpretable mapping between AV1 encoding parameters and decoding energy consumption—providing a practical, backward-compatible, and minimally intrusive paradigm for energy-efficient streaming in resource-constrained environments.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The widespread adoption of advanced video codecs such as AV1 is often hindered by their high decoding complexity, posing a challenge for battery-constrained devices. While encoders can be configured to produce bitstreams that are decoder-friendly, estimating the decoding complexity and energy overhead for a given video is non-trivial. In this study, we systematically analyse the impact of disabling various coding tools and adjusting coding parameters in two AV1 encoders, libaom-av1 and SVT-AV1. Using system-level energy measurement tools like RAPL (Running Average Power Limit), Intel SoC Watch (integrated with VTune profiler), we quantify the resulting trade-offs between decoding complexity, energy consumption, and compression efficiency for decoding a bitstream. Our results demonstrate that specific encoder configurations can substantially reduce decoding complexity with minimal perceptual quality degradation. For libaom-av1, disabling CDEF, an in-loop filter gives us a mean reduction in decoding cycles by 10%. For SVT-AV1, using the in-built, fast-decode=2 preset achieves a more substantial 24% reduction in decoding cycles. These findings provide strategies for content providers to lower the energy footprint of AV1 video streaming.

Problem

• Reducing AV1 decoder complexity for battery-constrained devices
• Quantifying trade-offs between decoding complexity and compression efficiency
• Optimizing encoder parameters to lower video streaming energy consumption

Innovation

• Tuning encoder parameters to reduce decoder complexity
• Disabling CDEF filter cuts decoding cycles by 10%
• Using fast-decode preset reduces cycles by 24%