  Numerous video frame sampling methodologies detailed in the literature
present a significant challenge in determining the optimal video frame method
for Video RAG pattern without a comparative side-by-side analysis. In this
work, we investigate the trade-offs in frame sampling methods for Video & Frame
Retrieval using natural language questions. We explore the balance between the
quantity of sampled frames and the retrieval recall score, aiming to identify
efficient video frame sampling strategies that maintain high retrieval efficacy
with reduced storage and processing demands. Our study focuses on the storage
and retrieval of image data (video frames) within a vector database required by
Video RAG pattern, comparing the effectiveness of various frame sampling
techniques. Our investigation indicates that the recall@k metric for both
text-to-video and text-to-frame retrieval tasks using various methods covered
as part of this work is comparable to or exceeds that of storing each frame
from the video. Our findings are intended to inform the selection of frame
sampling methods for practical Video RAG implementations, serving as a
springboard for innovative research in this domain.
