How does AI image processing perform image quality assessment?

AI image processing performs image quality assessment (IQA) by leveraging machine learning and deep learning techniques to evaluate the perceptual or technical quality of an image. The goal is to determine how "good" or "bad" an image is, often in terms of human visual perception or specific technical criteria like sharpness, noise, contrast, and distortion.

There are generally two types of IQA methods used in AI image processing:

1. Full-Reference IQA (FR-IQA):
   This method compares the distorted image with its original, high-quality reference image. The AI model analyzes differences between the two to predict a quality score. Common features include pixel-level differences, structural similarity, and frequency domain analysis.
   Example: A model might use a Convolutional Neural Network (CNN) to compare luminance, contrast, and structure between the reference and distorted image, similar to the SSIM (Structural Similarity Index) metric but learned from data.

2. No-Reference IQA (NR-IQA) or Blind IQA:
   In this scenario, only the distorted image is available, with no original for comparison. The AI model is trained on large datasets of images labeled with human-assigned quality scores to learn the features that correlate with perceived quality.
   Example: A deep learning model like a ResNet or Vision Transformer can be trained to directly predict a quality score from a single image by learning patterns associated with blur, compression artifacts, or noise.

3. Reduced-Reference IQA (RR-IQA):
   This is a hybrid approach where only partial information about the original image (like a feature map or a signature) is used along with the distorted image for assessment.

How AI Models Work in IQA:

• Feature Extraction: AI models extract high-level features related to human visual perception, such as edges, textures, and contrast.
• Training with Datasets: Models are trained using datasets like LIVE, TID2013, or KonIQ-10k, where images are rated by humans for quality.
• Prediction: Once trained, the model can predict a quality score (often a number between 0 and 1 or 0 and 100) for new, unseen images.

Example Use Case:
In an image enhancement application, before applying filters or upscaling, the system can assess the input image’s quality using NR-IQA. If the quality is too low, it might first apply denoising or super-resolution using AI models, then reassess the enhanced output.

Recommended Tencent Cloud Service:
For implementing AI-based image quality assessment, Tencent Cloud offers TI-Platform (Tencent Intelligent Platform) which provides tools for building, training, and deploying custom deep learning models. Additionally, Tencent Cloud TI-Insight and Machine Learning Platform for AI can help in training custom IQA models using your own datasets. For pre-trained models and accelerated inference, Tencent Cloud TI-Accelerator and Cloud GPU services are also useful to handle the computational load efficiently.