To meet the real-time analysis requirements of video streaming applications,
we propose an inter-relation-aware video complexity analyzer (IVCA) as an
extension to VCA. The IVCA addresses the limitation of VCA by considering
inter-frame relations, namely motion and reference structure. First, we enhance
the accuracy of temporal features by introducing feature-domain motion
estimation into the IVCA. Next, drawing inspiration from the hierarchical
reference structure in codecs, we design layer-aware weights to adjust the
majorities of frame complexity in different layers. Additionally, we expand the
scope of temporal features by considering frames that be referred to, rather
than relying solely on the previous frame. Experimental results show the
significant improvement in complexity estimation accuracy achieved by IVCA,
with minimal time complexity increase.

通过考虑帧间关系，我们提出了一种适用于视频流应用的关联感知视频复杂度分析器（IVCA）作为 VCA 的扩展。IVCA 通过引入特征域运动估计来提高时域特征的准确性，运用编解码器中的分层参考结构设计了层感知权重来调整不同层次的帧复杂度，并通过考虑引用的帧而不是仅依赖于之前的帧来扩展时域特征的范围。实验结果表明，IVCA 显著提高了复杂度估计的准确性，同时增加的时间复杂度很小。

IVCA: 视频复杂度互相关感知分析器

IVCA: Inter-Relation-Aware Video Complexity Analyzer

Over the past two decades, the surge in video streaming applications has been
fueled by the increasing accessibility of the internet and the growing demand
for network video. As users with varying internet speeds and devices seek
high-quality video, transcoding becomes essential for service providers. In
this paper, we introduce a parametric rate-distortion (R-D) transcoding model.
Our model excels at predicting transcoding distortion at various rates without
the need for encoding the video. This model serves as a versatile tool that can
be used to achieve visual quality improvement (in terms of PSNR) via
trans-sizing. Moreover, we use our model to identify visually lossless and
near-zero-slope bitrate ranges for an ingest video. Having this information
allows us to adjust the transcoding target bitrate while introducing visually
negligible quality degradations. By utilizing our model in this manner, quality
improvements up to 2 dB and bitrate savings of up to 46% of the original target
bitrate are possible. Experimental results demonstrate the efficacy of our
model in video transcoding rate distortion prediction.

通过我们提出的参数化失真率（R-D）转码模型，我们可以在不需要对视频进行编码的情况下准确预测不同速率的转码失真，并通过转尺寸实现视觉质量的改进。此外，我们还利用该模型确定了摄入视频的近似无损和接近零斜率的比特率范围，从而在引入几乎不可见的质量损失的同时调整转码目标比特率。实验结果证明了我们的模型在视频转码率失真预测方面的有效性。