Skip to Main Content
A novel dynamic programming based technique for optimal selection of input video format and compression rate for video streaming based on "relevancy" of the content and user context is presented. The technique uses context dependent content analysis to divide the input video into temporal segments. User selected relevance levels (weights) are refined by using audio information and assigned to these segments. The weights are used in formulating a constrained optimization problem, which is solved using dynamic programming. The technique minimizes a weighted distortion measure and the initial waiting time for continuous playback under maximum acceptable distortion constraints. Spatial resolution, frame rate and average audio volume of the temporal segments and the DCT quantization parameters are used as optimization variables.