Image Evaluation

A guide on using OpenCV and Python to calculate image noise metrics for improved image quality and automated content assessment.

Discover how Large Multi-Modal Models (LMMs) can revolutionize image evaluation and selection in content pipelines. This guide explains the relevance of LMMs, image characteristics, quality metrics, and AI evaluation techniques.

This article explores the capabilities of multi-modal large language models (LLMs) in understanding content across various formats. How to extract scenes from an animated GIF.

Explore how CLIP, an innovative AI model by OpenAI, generates objective aesthetic scores for artwork. Discover its implications for the art world, from democratizing art evaluation to uncovering new talent and enhancing art education.

CLIP Score measures similarity between AI-generated image and text caption, useful for computer vision and language tasks. CLIP allows models to understand visual-textual relationships and perform image captioning and retrieval. To compute CLIP score, use pre-trained CLIP model to produce embeddings and calculate cosine similarity.