感知優化與評估之間未預見的不對稱性

摘要

感知優化主要由保真度目標驅動，該目標同時強制語意一致性和整體視覺真實感，而對抗性目標則通過增強感知銳度和細粒度細節提供互補的改進。儘管它們扮演著核心角色，但它們作為優化目標的有效性與其作為圖像質量評估（IQA）指標的能力之間的關聯性仍未得到充分探索。在本研究中，我們進行了系統性分析，並揭示了感知優化與評估之間的一種未預料的不對稱性：在IQA中表現出色的保真度指標並不一定對感知優化有效，這種不對齊在對抗訓練下更為明顯。此外，雖然鑑別器在優化過程中能有效抑制偽影，但其學習到的表示在重新用作IQA模型的主幹初始化時，提供的益處有限。除了這種不對稱性，我們的研究結果進一步表明，鑑別器設計在塑造優化過程中起著決定性作用，其中基於局部區域和卷積的架構比基於普通或Transformer的替代方案能提供更忠實的細節重建。這些見解深化了對損失函數設計及其與IQA可遷移性聯繫的理解，為更原則性的感知優化方法鋪平了道路。

English

Perceptual optimization is primarily driven by the fidelity objective, which enforces both semantic consistency and overall visual realism, while the adversarial objective provides complementary refinement by enhancing perceptual sharpness and fine-grained detail. Despite their central role, the correlation between their effectiveness as optimization objectives and their capability as image quality assessment (IQA) metrics remains underexplored. In this work, we conduct a systematic analysis and reveal an unanticipated asymmetry between perceptual optimization and assessment: fidelity metrics that excel in IQA are not necessarily effective for perceptual optimization, with this misalignment emerging more distinctly under adversarial training. In addition, while discriminators effectively suppress artifacts during optimization, their learned representations offer only limited benefits when reused as backbone initializations for IQA models. Beyond this asymmetry, our findings further demonstrate that discriminator design plays a decisive role in shaping optimization, with patch-level and convolutional architectures providing more faithful detail reconstruction than vanilla or Transformer-based alternatives. These insights advance the understanding of loss function design and its connection to IQA transferability, paving the way for more principled approaches to perceptual optimization.

感知優化與評估之間未預見的不對稱性

The Unanticipated Asymmetry Between Perceptual Optimization and Assessment

摘要

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