To optimize disk usage in video vector storage, focus on compression, deduplication, and efficient storage formats. Video vectors—compact numerical representations of video content—require careful handling to balance storage efficiency with retrieval performance. Key strategies include reducing vector size through compression, eliminating redundant data, and using storage formats designed for high-density data.
First, apply compression techniques like quantization and dimensionality reduction. Quantization reduces the precision of vector values—for example, converting 32-bit floating-point numbers to 8-bit integers—which can shrink storage needs by 75% without significant loss of accuracy. Dimensionality reduction methods like PCA (Principal Component Analysis) or autoencoders trim unnecessary features. For instance, a 1,024-dimensional vector might be reduced to 128 dimensions while retaining most of its representational power. Tools like Facebook’s FAISS library support product quantization, which splits vectors into sub-vectors and compresses them using shared codebooks. This approach minimizes redundancy and maintains search efficiency.
Second, deduplication and delta encoding help eliminate redundant data. Video frames or sequences often repeat, especially in static scenes or fixed camera angles. By hashing vectors (e.g., using SHA-256) and storing only unique entries, you avoid redundant copies. For time-series vectors (e.g., video embeddings per frame), delta encoding stores only the differences between consecutive vectors instead of full copies. For example, if a scene’s background remains unchanged, only the moving foreground elements need updates. This reduces storage overhead for sequential data and works well with formats like Apache Parquet, which support columnar storage and efficient delta encoding.
Finally, choose storage formats and databases optimized for vectors. Binary formats like Protocol Buffers or HDF5 serialize vectors more efficiently than JSON or CSV, reducing metadata overhead. Columnar formats like Parquet compress data further using algorithms like Snappy or Zstd. Vector databases such as Milvus or Pinecone natively integrate compression and indexing, allowing you to store vectors in compressed formats (e.g., IVF_PQ in FAISS) while enabling fast retrieval. For example, storing 1 million vectors as 8-bit integers in Parquet with Zstd compression might use 80% less space than uncompressed float32 arrays in a CSV file. These methods ensure minimal disk usage without sacrificing usability for tasks like similarity search.