# Scene Perceived Image Perceptual Score (SPIPS): combining global and local perception for image quality assessment

arXiv: https://arxiv.org/abs/2504.17234 · HTML: https://mlsanity.com/paper/2504.17234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [Image Quality Assessment using Contrastive Learning](https://mlsanity.com/paper/2110.13266/) (2021)
- [A very preliminary analysis of DALL-E 2](https://mlsanity.com/paper/2204.13807/) (2022)
- [Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach](https://mlsanity.com/paper/1609.04757/) (2016)
- [Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training](https://mlsanity.com/paper/2105.14550/) (2021)
- [Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network](https://mlsanity.com/paper/2204.10485/) (2022)
- [Data-Efficient Image Quality Assessment with Attention-Panel Decoder](https://mlsanity.com/paper/2304.04952/) (2023)
- [Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment](https://mlsanity.com/paper/2108.07948/) (2021)
- [Do better ImageNet classifiers assess perceptual similarity better?](https://mlsanity.com/paper/2203.04946/) (2022)
- [Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity](https://mlsanity.com/paper/2207.08689/) (2022)
- [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022)
- [Learned Perceptual Image Enhancement](https://mlsanity.com/paper/1712.02864/) (2017)
- [Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion](https://mlsanity.com/paper/2305.03509/) (2023)

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning](https://mlsanity.com/paper/2602.10744/) (2026)

## Abstract (arXiv)

The rapid advancement of artificial intelligence and widespread use of smartphones have resulted in an exponential growth of image data, both real (camera-captured) and virtual (AI-generated). This surge underscores the critical need for robust image quality assessment (IQA) methods that accurately reflect human visual perception. Traditional IQA techniques primarily rely on spatial features - such as signal-to-noise ratio, local structural distortions, and texture inconsistencies - to identify artifacts. While effective for unprocessed or conventionally altered images, these methods fall short in the context of modern image post-processing powered by deep neural networks (DNNs). The rise of DNN-based models for image generation, enhancement, and restoration has significantly improved visual quality, yet made accurate assessment increasingly complex. To address this, we propose a novel IQA approach that bridges the gap between deep learning methods and human perception. Our model disentangles deep features into high-level semantic information and low-level perceptual details, treating each stream separately. These features are then combined with conventional IQA metrics to provide a more comprehensive evaluation framework. This hybrid design enables the model to assess both global context and intricate image details, better reflecting the human visual process, which first interprets overall structure before attending to fine-grained elements. The final stage employs a multilayer perceptron (MLP) to map the integrated features into a concise quality score. Experimental results demonstrate that our method achieves improved consistency with human perceptual judgments compared to existing IQA models.