arXiv:2408.03340v1 Announce Type: new
Abstract: 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.
Investigating the Trade-offs in Frame Sampling Methods for Video & Frame Retrieval
In the field of multimedia information systems, video frame sampling plays a crucial role in Video & Frame Retrieval. However, the myriad of methodologies available in the literature makes it challenging to determine the optimal video frame sampling method for the Video RAG pattern. This work seeks to address this challenge by conducting a comparative analysis of frame sampling techniques, examining the trade-offs between the quantity of sampled frames and the retrieval recall score.
One of the key objectives of this study is to identify efficient video frame sampling strategies that maintain high retrieval efficacy while reducing storage and processing demands. This aligns with the multi-disciplinary nature of the concepts explored in multimedia information systems, as it combines elements from fields such as computer vision, natural language processing, and information retrieval.
The focus of this investigation is the storage and retrieval of image data (video frames) within a vector database required by Video RAG pattern. By comparing the effectiveness of various frame sampling techniques, the study aims to provide insights into the most suitable approach for practical Video RAG implementations.
An important aspect of this research is the examination of the recall@k metric for both text-to-video and text-to-frame retrieval tasks. This metric measures the ability of a retrieval system to rank relevant frames or videos within the top-k results. The findings of this study demonstrate that the recall@k metric, when utilizing the methods covered in this work, is comparable to or exceeds that of storing each frame from the video.
This study’s significance to the wider field of multimedia information systems lies in its potential to inform the selection of frame sampling methods for video retrieval and analysis. By identifying strategies that achieve high retrieval efficacy while reducing storage and processing demands, this research contributes to the development of more efficient multimedia systems.
Furthermore, the investigation of frame sampling methods also relates to the domains of animations, artificial reality, augmented reality, and virtual realities. These fields often rely on multimedia information systems to create and deliver immersive experiences. Understanding the trade-offs in frame sampling methods can aid in the creation of more realistic and interactive virtual environments.
In conclusion, this work provides valuable insights into the trade-offs involved in frame sampling methods for Video & Frame Retrieval. By balancing the quantity of sampled frames and the retrieval recall score, this study identifies efficient strategies that can enhance the performance of multimedia information systems. With its multi-disciplinary nature and relevance to various domains, this research serves as a foundation for further innovative advancements in the field.