发表机构
Université du Québec à Rimouski; Kingston University London（魁北克大学里穆斯基分校; 金斯顿大学伦敦校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对流媒体编码中SSIM计算开销大的问题，提出ApproxSSIMate方法，利用PSNR和预计算统计量估计SSIM，解耦质量评估，显著降低开销，并在多种编解码器上验证有效性。
AI 中文摘要
现代流媒体流水线对每个资产运行数百个候选编码，以支持每标题编码、基于镜头的优化和上下文自适应的ABR阶梯构建。这些技术已将感知质量指标置于关键路径中：SSIM和VMAF现在指导编码决策，而非被动监控。我们测量到，在生产速度预设下，SSIM评估占x264编码时间的7-35%，且随着编码器运行速度加快，该成本比例上升。我们提出ApproxSSIMate，一种低复杂度方法，从PSNR结合参考序列统计信息估计SSIM，这些统计信息对每个序列计算一次，并在所有候选编码中复用。这将质量估计从编码-解码-比较循环中解耦，使实时编码中的感知质量反馈成为可能，并在每标题工作流中摊销跨候选编码的质量测量。我们在Objective-1-fast数据集上验证了该方法在H.264/AVC、H.265/HEVC和AV1上的有效性，并将实现作为免费开源软件发布。
英文摘要
Modern streaming pipelines run hundreds of candidate encodes per asset to support per-title encoding, shot-based optimization, and context-adaptive ABR ladder construction. These techniques have moved perceptual quality metrics into the critical path: SSIM and VMAF now guide encoding decisions rather than passively monitor them. We measure that SSIM evaluation accounts for 7-35% of x264 encode time at production speed presets, with the cost ratio rising as encoders run faster. We propose ApproxSSIMate, a low-complexity method for estimating SSIM from PSNR combined with reference-sequence statistics computed once per sequence and reused across every candidate encode. This decouples quality estimation from the encode-decode-compare loop, enabling perceptual quality feedback in live encoding and amortizing quality measurement across candidate encodes in per-title workflows. We validate the approach across H.264/AVC, H.265/HEVC, and AV1 on the Objective-1-fast dataset and release the implementation as free and open-source software.
CommentsPresented at IBC 2026, Amsterdam, The Netherlands. 2026 IBC Technical Paper
