TVM: A Tile-based Video Management Framework

With the exponential growth of video data, there is a pressing need for ecient video analysis technology. Modern query frameworks aim to accelerate queries by reducing the frequency of calls to expensive deep neural networks, which often overlook the overhead associated with video decoding and retrieval. Furthermore, video storage frameworks optimize video retrieval through video partition or caching, often relying on prior information about the query workload. To further accelerate queries, this study introduces a novel tile-based video management framework, called TVM, which leverages the semantic information embedded in videos, without being dependent on specic query workloads. By constructing a tile-based semantic index for newly ingested videos, TVM eectively reduces the size of decoded and processed video data. To achieve this, TVM introduces an optimal index construction algorithm that utilizes cost function and pseudo-labels. Additionally, the framework proposes a query-driven tile parallel decoding algorithm and resource caching algorithms, which further expedite the retrieval of video frames. Experimental results demonstrate that TVM can signicantly enhance the throughput of various query tasks, achieving a notable speedup of more than 5.6⇥.