# Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?

arXiv: https://arxiv.org/abs/2405.20392 · HTML: https://mlsanity.com/paper/2405.20392/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 28 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 22 of 22)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)
- [On the Generalization of BasicVSR++ to Video Deblurring and Denoising](https://mlsanity.com/paper/2204.05308/) (2022)
- [Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks](https://mlsanity.com/paper/2310.06958/) (2023)
- [A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions](https://mlsanity.com/paper/2302.04032/) (2023)
- [iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks](https://mlsanity.com/paper/2006.11161/) (2020)
- [Perceptual impact of the loss function on deep-learning image coding performance](https://mlsanity.com/paper/2311.06084/) (2023)

## Built on by (most cited first, 4 of 4)

- [Cross-Modal Transferable Image-to-Video Attack on Video Quality Metrics](https://mlsanity.com/paper/2501.08415/) (2025)
- [Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics](https://mlsanity.com/paper/2408.01541/) (2024)
- [BiRQA: Bidirectional Robust Quality Assessment for Images](https://mlsanity.com/paper/2602.20351/) (2026)
- [Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment](https://mlsanity.com/paper/2411.12575/) (2024)

## Abstract (arXiv)

Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptual losses is often limited to LPIPS, a fullreference method. Even though deep no-reference image-qualityassessment methods are excellent at predicting human judgment, little research has examined their incorporation in loss functions. This paper investigates direct optimization of several video-superresolution models using no-reference image-quality-assessment methods as perceptual losses. Our experimental results show that straightforward optimization of these methods produce artifacts, but a special training procedure can mitigate them.