Learning to generate images with perceptual similarity metrics
Open accessAbstract
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compact representation. Supervised training of image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the mismatch between a generated image and its corresponding target image. We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM) [1]. Because MS-SSIM is differentiable, it is easily incorporated into gradient-descent learning. We compare the consequences of using MS-SSIM versus PL loss on training autoencoders. Human observers reliably prefer images synthesized by MS-SSIM-optimized models over those synthesized by PL-optimized models, for two distinct PL measures (L1and L2distances). We also explore the effect of training objective on image encoding and analyze conditions under which perceptually-optimized representations yield better performance on image classification. Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. We argue that significant advances can be made in modeling images through the use of training objectives that are well aligned to characteristics of human perception.
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Modern deep-learning metrics like LPIPS offer better perceptual alignment with human judgment but remain computationally prohibitive for real-time, resource-constrained environments.
“Conversely, modern deep-learning metrics like LPIPS offer better perceptual alignment but remain computationally prohibitive for real-time, resource-constrained environments.”
From A Survey and Tutorial on Image Quality Assessment with a Contrast-Weighted Structural Similarity Framework (2026) · cites this paper as [30]
The Abstract and §7 report that human observers preferred images from SSIM/MS-SSIM-optimized networks over those from pixel-loss-optimized networks, supporting the general point about perceptual alignment. The paper does not evaluate LPIPS or say that perceptual metrics are computationally prohibitive in real-time, resource-constrained settings. Its discussion of computational cost in §2.1 concerns training Boltzmann-machine models instead. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDeep learning-based similarity metrics such as LPIPS correlate strongly with human judgment but incur high computational costs, offer low interpretability, and are vulnerable to adversarial perturbations.
“Deep learning-based similarity [29-34]: Semantic similarity learning uses convolutional neural networks (CNNs) to embed images in a structured feature space. Metrics such as LPIPS (learned perceptual image patch similarity) correlate strongly with human judgment but incur high computational costs, offer low interpretability (a 'black-box' nature), and are vulnerable to adversarial perturbations.”
From A Survey and Tutorial on Image Quality Assessment with a Contrast-Weighted Structural Similarity Framework (2026) · cites this paper as [30]
Section 3.2.2 reports that human judges ranked MS-SSIM-optimized reconstructions highest for 709 of 1,000 images, supporting the broader point that perceptually grounded methods can align with human judgments. The paper does not establish that LPIPS correlates strongly with human judgment, or address LPIPS’s computational cost, interpretability, or vulnerability to adversarial perturbations. Its Discussion mentions “black-box loss functions” as a modeling possibility, not as evidence of low interpretability. (LLM, conf=0.86)
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Synthesizing human-like sketches from natural images using a conditional convolutional decoder
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Detection of Anomalous Grapevine Berries Using Variational Autoencoders
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Generative Smoke RemovalPreprint
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Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks
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Smart GAN: a smart generative adversarial network for limited imbalanced dataset
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Radar Sensor Simulation with Generative Adversarial Network
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Learning a multiscale patch-based representation for image denoising in X-RAY fluoroscopy
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Multi-task generative adversarial network for retinal optical coherence tomography image denoising
Xie, Q., Ma, Z., Zhu, L., Fan, F., Meng, X., Gao, X., & Zhu, J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac944a · Cited by 13 · see citing works
Guan, S., & Loew, M. H. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06031-5 · Cited by 13 · see citing works
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105017 · Cited by 12 · see citing works
Arnold, S., & Yamazaki, K. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00022 · Cited by 12 · see citing works
CNN-Based Metrics for Performance Evaluation of Generative Adversarial Networks
Behera, A. P., Prakash, S., Khanna, S., Nigam, S., & Verma, S. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3401650 · Cited by 11 · see citing works
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CAGAN: Text-To-Image Generation with Combined Attention Generative Adversarial Networks
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Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems
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Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network
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Perceptually Optimizing Deep Image CompressionPreprint
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A Variational Auto-Encoder Approach for Image Transmission in Noisy Channel
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A Single-Shot Harmonic Imaging Approach Utilizing Deep Learning for Medical Ultrasound
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Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches
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PixRevive: Latent Feature Diffusion Model for Compressed Video Quality Enhancement
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Generating High-Fidelity Images with Disentangled Adversarial VAEs and Structure-Aware Loss
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Image2GIF: Generating Cinemagraphs Using Recurrent Deep Q-Networks
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Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use CasePreprint
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Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission
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Scalable Font Reconstruction with Dual Latent Manifolds
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An image interpolation approach for acquisition time reduction in navigator-based 4D MRI
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Dynamics of Fourier Modes in Torus Generative Adversarial Networks
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Temporal Interpolation via Motion Field PredictionPreprint
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Adversarially Trained Variational Auto-Encoders With Maximum Mean Discrepancy based Regularization
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A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images
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Approximating Human Judgment of Generated Image QualityPreprint
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Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks
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Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems
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- Authors
- Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., & Zemel, R. S.
- Affiliations
- University of Toronto (CA)
- University of Colorado Boulder (US)
- Venue
- 2017 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 4277-4281
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2174358748
- Semantic Scholar
- 90e4f12fa8fb126d0f416c8b61bfb5f73f8b7b74
- Cited by
- 186
- References
- 46
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 21 · 2023: 21 · 2022: 17 · 2021: 22 · 2020: 28 · 2019: 16 · 2018: 11 · 2017: 7 · 2016: 4 · 2015: 1
- FWCI
- 4.96
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0542013, 1041755, 1461535)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- perceptual image quality, MS-SSIM, image generation, loss function design, autoencoder, image reconstruction, image classification, super-resolution, human perception
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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