Spatio-temporal indexing of vector quantized video sequences

Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. Vector quantization (VQ) is an efficient technique for low bit-rate image and video compression. In addition, the low complexity of the decoder makes VQ attractive for low power systems and applications which require fast decoding. In this paper, we present an indexing technique for compressed video using vector quantization. Here, a video sequence is first compressed using VQ. Each frame is represented by a usage map, a set of VQ labels, and a set of motion vectors. The video sequence is partitioned into shots and the various camera operations and motion within each shot are then determined by processing the VQ label maps. Each shot is indexed using a spatio-temporal index. The spatial index refers to the spatial content of the representative frame of a shot, while the temporal index represents the temporal content of the shot. The spatial index is based on the codewords used to compress the representative frame, while the temporal index is based on motion and camera operations within the shot. The proposed indexing technique is executed entirely in the compressed domain. This entails significant savings in computational and storage costs resulting in faster execution.