The Unanticipated Asymmetry Between Perceptual Optimization and
  Assessment
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.
68d6116f8ccd91bdd39ffe15
Jiabei Zhang
admin_assigned
2025-09-26T16:16:09.329Z
68381e6d4d14d7c8801717b8
/avatars/8a080a965b30d002c956050359800814.svg
Jiabei Zhang
user
Oreki1999
68d6116f8ccd91bdd39ffe16
Qi Wang
68d6116f8ccd91bdd39ffe17
Siyu Wu
admin_assigned
2025-09-26T16:16:18.865Z
6516fbf169c938b3d6dc5889
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vLW8vXEagWPiMnj04hfYf.jpeg
Siyu Wu
user
siyuwu4
68d6116f8ccd91bdd39ffe18
Du Chen
68d6116f8ccd91bdd39ffe19
Tianhe Wu
claimed_verified
2025-09-26T12:09:06.486Z
655de51982afda0fc479fb91
https://cdn-avatars.huggingface.co/v1/production/uploads/655de51982afda0fc479fb91/-t9RLNEBAESO0niQGHoss.png
Tianhe Wu
user
TianheWu
The Unanticipated Asymmetry Between Perceptual Optimization and
  Assessment
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.
The study reveals an asymmetry between perceptual optimization and image quality assessment, showing that effective IQA metrics are not always suitable for perceptual optimization, especially under adversarial training, and highlights the importance of discriminator design in optimization.
perceptual optimization fidelity objective adversarial objective perceptual sharpness image quality assessment (IQA) discriminators patch-level architectures convolutional architectures Transformer-based architectures loss function design IQA transferability