A metric for Perceptual Image-Error Assessment through Pairwise Preference (PieAPP at CVPR 2018).
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Updated
Jan 5, 2024 - Python
A metric for Perceptual Image-Error Assessment through Pairwise Preference (PieAPP at CVPR 2018).
[ECCV 2024] Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models
[ Official ] - PIPAL Dataset and Training Codebase. ECCV-2020, NTIRE-21/22.
This is the official implementation for εar-VAE model including inference and evaluation parts, more details coming soon...
Learning-based Just-noticeable-quantization-distortion Model for perceptual video coding
Code for "Adversarial and Perceptual Refinement Compressed Sensing MRI Reconstruction"
Official code (Pytorch) for paper Perception-Enhanced Single Image Super-Resolution via Relativistic Generative Networks
Supplementary material for the paper "BL-JUNIPER: A CNN Assisted Framework for Perceptual Video Coding Leveraging Block Level JND", IEEE TMM 2022
Full-Reference Image Quality Assessment models based on ensemble of gradient boosting
Supplementary material for the paper "PERCEPTUAL LEARNED IMAGE COMPRESSION VIA END-TO-END JND-BASED OPTIMIZATION", IEEE ICIP 2024
Supplementary material for the paper "Lightweight Multitask Learning for Robust JND Prediction using Latent Space and Reconstructed Frames", IEEE TCSVT, 2024.
Supplementary material for the paper "MTJND: MULTI-TASK DEEP LEARNING FRAMEWORK FOR IMPROVED JND PREDICTION", IEEE ICIP 2023
Full-reference objective quality index for reconstructed background images.
A Java port of the perceptualdiff image comparison (pdiff.sourceforge.net)
Super Resolution
A fast, no-reference video quality benchmarking tool using BRISQUE and other IQA metrics. Extracts sampled frames, computes perceptual quality scores, and compares encodes objectively.
Gram-GAN bridges pixel-based and GAN SR methods, generating sharp and realistic textures by explicitly matching generated patches to real HR textures via a Gram-based patch database. It ensures stable training, better generalization, and texture-aware outputs—ideal for satellite, medical, art, and fashion super-resolution tasks.
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