Relevancy-Guided Adaptive Decoupling for Multi-Module Joint Optimization in Perceptual Video Coding

In professional broadcasting and Over-the-Top (OTT) streaming, non-linear coupling among perceptual modules in hybrid architectures fundamentally limits end-user Quality of Experience (QoE). Current heuristic tuning and brute-force searches cannot properly model cross-module dependencies. Encoders thus often fall into sub-optimal local minima. We propose a systematic relevancy-guided decoupling framework for perceptual video coding to address this issue. We introduce a quantitative Inter-module Relevancy Metric based on a full-factorial design. This metric explicitly models the interaction intensity among heterogeneous coding modules. Guided by this metric, our Influence-Based Ascending Optimization (IBAO) strategy transforms the NP-hard joint optimization into a deterministic sequential tuning path that mitigates cumulative errors. We also design a strictly bounded online adaptive parameter model to address video non-stationarity. It tracks local optimal operating points in real time with minimal runtime overhead. Experiments on the industrial HEVC/x265 platform validate an average 1.94 BD-VMAF gain over the standard x265 Slow preset. Critically, pixel-level objective fidelity remains undegraded. These results confirm our approach effectively resolves inter-module coupling conflicts. Ultimately, it delivers a stable, Pareto-optimal rate-distortion solution suited for real-world multimedia distribution.