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Adaptive Resolution and Chroma Subsampling for Energy-Efficient Video Coding

Amritha Premkumar, Christian Herglotz

TL;DR

ARCS tackles the inefficiency of fixed chroma subsampling by jointly optimizing spatial resolution and chroma format for each target bitrate, aiming to reduce decoding energy without perceptual loss. It defines a normalized quality–complexity objective $J'$ that balances perceptual quality and decoding time via a tunable parameter $\alpha$, and enforces monotonicity constraints to ensure stable bitrate ladders. The framework generates multiple candidate representations, evaluates them with objective metrics such as ColorVideoVDP and decoding time, and selects the best $(r,c)$ pairs per bitrate. Experimental results on 15 SJTU UHD sequences show substantial decoding-time reductions (up to about $75\%$) with small or moderate bitrate penalties, demonstrating chroma adaptivity as a practical lever for energy-efficient streaming. ARCS is codec-agnostic and integrates naturally into per-title or per-segment workflows, offering a new control dimension for energy-aware video delivery.

Abstract

Conventional video encoders typically employ a fixed chroma subsampling format, such as YUV420, which may not optimally reflect variations in chroma detail across different types of content. This can lead to suboptimal chroma quality and inefficiencies in bitrate allocation. We propose an Adaptive Resolution-Chroma Subsampling (ARCS) framework that jointly optimizes spatial resolution and chroma subsampling to balance perceptual quality and decoding efficiency. ARCS selects an optimal (resolution, chroma format) pair for each bitrate by maximizing a composite quality-complexity objective, while enforcing monotonicity constraints to ensure smooth transitions between representations. Experimental results using x265 show that, compared to a fixed-format encoding (YUV444), on average, ARCS achieves a 13.48 % bitrate savings and a 62.18 % reduction in decoding time, which we use as a proxy for the decoding energy, to yield the same colorVideoVDP score. The proposed framework introduces chroma adaptivity as a new control dimension for energy-efficient video streaming.

Adaptive Resolution and Chroma Subsampling for Energy-Efficient Video Coding

TL;DR

ARCS tackles the inefficiency of fixed chroma subsampling by jointly optimizing spatial resolution and chroma format for each target bitrate, aiming to reduce decoding energy without perceptual loss. It defines a normalized quality–complexity objective that balances perceptual quality and decoding time via a tunable parameter , and enforces monotonicity constraints to ensure stable bitrate ladders. The framework generates multiple candidate representations, evaluates them with objective metrics such as ColorVideoVDP and decoding time, and selects the best pairs per bitrate. Experimental results on 15 SJTU UHD sequences show substantial decoding-time reductions (up to about ) with small or moderate bitrate penalties, demonstrating chroma adaptivity as a practical lever for energy-efficient streaming. ARCS is codec-agnostic and integrates naturally into per-title or per-segment workflows, offering a new control dimension for energy-aware video delivery.

Abstract

Conventional video encoders typically employ a fixed chroma subsampling format, such as YUV420, which may not optimally reflect variations in chroma detail across different types of content. This can lead to suboptimal chroma quality and inefficiencies in bitrate allocation. We propose an Adaptive Resolution-Chroma Subsampling (ARCS) framework that jointly optimizes spatial resolution and chroma subsampling to balance perceptual quality and decoding efficiency. ARCS selects an optimal (resolution, chroma format) pair for each bitrate by maximizing a composite quality-complexity objective, while enforcing monotonicity constraints to ensure smooth transitions between representations. Experimental results using x265 show that, compared to a fixed-format encoding (YUV444), on average, ARCS achieves a 13.48 % bitrate savings and a 62.18 % reduction in decoding time, which we use as a proxy for the decoding energy, to yield the same colorVideoVDP score. The proposed framework introduces chroma adaptivity as a new control dimension for energy-efficient video streaming.
Paper Structure (17 sections, 3 equations, 4 figures, 2 tables)

This paper contains 17 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Adaptive Resolution Chroma Subsampling (ARCS) Framework
  3. Generation of subsampled videos
  4. Encoding and Decoding Evaluation
  5. Quality–Complexity Modeling
  6. Adaptive Decision Optimization
  7. Evaluation Setup
  8. Dataset
  9. Metrics
  10. Protocol and Implementation Details
  11. Benchmarks
  12. Experimental Results
  13. Rate–Quality–Complexity Behavior
  14. Effect of the Regularization Parameter $\alpha$
  15. Ablation Study: Impact of Chroma Adaptivity
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Rate–Quality and Rate–Time curves for the Campfire Party sequence sjtu_video_ref encoded with x265 at ultra high definition UHD using YUV444, YUV422, and YUV420 formats. "Time" denotes decoding time, and "Quality" denotes colorVideoVDP in Just Objectionable Difference (JOD) units ColorVideoVDP_ref.
  • Figure 2: Overview of the ARCS methodology.
  • Figure 3: Rate-colorVideoVDP, and rate-decoding time curves of two representative sequences.
  • Figure 4: Results for various $\alpha$ values.