A Reference-Free Framework for Evaluating Single-Frame ISP Pipelines

2026-07-25

AI Summary

This work addresses the challenge that existing no-reference image quality assessment (IQA) methods struggle to accurately evaluate low-level artifacts introduced by camera image signal processors (ISPs), while full-reference metrics require pristine reference images that are often unavailable. To overcome this limitation, we propose a novel framework that leverages a single sRGB image along with its ISO metadata to synthesize a proxy reference image, enabling the computation of standard full-reference metrics such as PSNR, SSIM, and LPIPS without access to a ground-truth reference. By combining synthetic data pretraining with lightweight LoRA fine-tuning, our method rapidly adapts to diverse ISP configurations and significantly outperforms conventional no-reference IQA approaches and direct regression strategies on real-world camera data, achieving notable improvements in both metric estimation accuracy and ranking consistency.

Abstract

Evaluating camera image signal processing (ISP) pipelines requires measuring low-level artifacts introduced by operations such as denoising, demosaicing, tone mapping, and compression. Blind image quality assessment (IQA) techniques can grade visual quality without a reference, but they typically focus on semantic and high-level visual cues or human perceptual scores rather than the low-level image-processing artifacts introduced by camera pipelines. In contrast, full-reference metrics such as PSNR and SSIM measure pixel-level differences and structural similarity, while LPIPS measures perceptual similarity in deep feature space. However, these metrics require perfectly aligned image pairs, which are difficult to collect in practical settings. We propose a reference-free learning framework that estimates full-reference image quality metrics from a processed sRGB image and its ISO metadata. Our method predicts a proxy sRGB reference, which is then compared with the processed image to compute PSNR, SSIM, and LPIPS in their standard full-reference form. Our experiments show that the proxy-reference model can be learned from synthetic data and applied to real camera data. We further show that lightweight LoRA fine-tuning enables efficient adaptation when ISP components or pipeline configurations are changed. The proposed method outperforms direct metric regression in estimating metric values and achieves higher agreement with full-reference rankings than conventional blind IQA methods. These results demonstrate the feasibility of reference-free estimation of full-reference metrics for practical camera-pipeline evaluation.

Problem

• ISP pipeline evaluation
• reference-free IQA
• low-level artifacts
• full-reference metrics
• image quality assessment

Innovation

• reference-free evaluation
• proxy reference
• ISP pipeline
• blind IQA
• LoRA fine-tuning