richzhang/PerceptualSimilarity
PerceptualSimilarity
PerceptualSimilarity 是一个深度学习框架，旨在量化和评估图像之间的感知距离。它提供了一个系统，通过使用深度特征表示而不是像素级差异来衡量两张图像或图像块在人类视觉中看起来有多相似。
该项目实现了一个可微分距离度量，作为损失函数，允许通过反向传播优化图像像素以达到目标视觉外观。它包括一个可训练的线性层，可以应用于冻结的深度特征，以学习与人类感知一致的加权距离度量。
该框架涵盖了图像质量评估、相似度度量训练和计算机视觉基准测试方面的广泛功能。模型准确性通过使用诸如二选一强制选择测试等框架，将预测的距离分数与人类判断数据集进行比较来评估。
Features
- Perceptual Loss - Provides a differentiable perceptual loss function based on feature similarity for visual quality optimization.
- Computer Vision Models - Provides a deep learning framework for training and evaluating models focused on perceptual similarity.
- Deep Feature Distances - Implements similarity calculations based on Euclidean distances between high-level deep feature maps.
- Metric Learning - Trains linear layers on top of image representations to create custom metrics for visual similarity.
- Perceptual Metric Heads - Uses a trainable linear layer on top of frozen features to learn weighted human-perceptual distances.
- Perceptual Metrics - Calculates the distance between images based on human visual perception using deep feature representations.
- Image Quality Assessment - Compares processed images against originals to evaluate if visual artifacts are noticeable to humans.
- Perceptual Similarity Scoring - Calculates numerical values to quantify the perceived difference between images using deep learning features.
- Visual Similarity Training - Allows creating linear layers on image representations or optimizing models using specific visual training datasets.
- Computer Vision Benchmarks - Tests the accuracy of visual similarity models against standardized human judgment datasets.
- Iterative Image Optimizers - Implements iterative gradient descent to refine image pixels by minimizing perceptual distance.
- Two-Alternative Forced Choice Tests - Evaluates model accuracy by comparing predicted distance scores against human preferences in a binary choice framework.
- Perceptual Image Optimization - Adjusting the pixels of an image by using perceptual distance as a loss function to reach a target visual look.
- Perceptual Model Evaluation - Tests similarity models against standard datasets using human judgment modes like just-noticeable difference.
- Computer Vision Research - Deep features as a metric for perceptual image similarity.
- Evaluation Metrics - Learned perceptual image patch similarity metric.
- Image Transformation - Deep features as a perceptual metric.
