There are increasing demands for ultralow bit-rate video image transmission. We present a new formulation of moving object segmentation and motion estimation to quantify the potential gain in using object-oriented motion compensation in image sequence coding. Motivated by real-valued parameter estimation required in object-oriented motion-compensated video source coding, a framework motivated by Rissanen's (1983) minimum description length (MDL) principle is proposed to more tightly couple motion estimation and object segmentation algorithms to the overall objective of minimizing source bit rate. A new objective function is constructed, and a suboptimal procedure to segment and estimate moving objects in a scene is proposed. Each object is represented by chain-coded block boundaries, affine motion parameters, and motion-compensated prediction error. A number of experimental comparisons between block- and object-oriented coding schemes suggests a significant potential coding gain using object-oriented motion-compensated coding.

Presents a new formulation of moving object segmentation and motion estimation to quantify the potential gain in using object-oriented motion compensation in image sequence coding. Motivated by Rissanen's (1983) minimum description length (MDL) principle that allows unified treatment of coding model complexity and parameter values, an objective function is constructed in terms of an ideal coding length function for each motion-compensated frame. Based on this new objective function, a procedure to segment and estimate moving objects in a scene is proposed. Each object in the scene is represented by its boundaries, motion parameters, and motion-compensated prediction error (MCPE). A number of experimental comparisons between block-oriented and object oriented coding schemes suggest that significant potential coding gain using the new MDL-based criterion is possible.>

Local motion measurement errors are used to guide the global smoothing process in order to preserve motion-field discontinuities. A field-smoothing algorithm based on matching-error weighting is proposed. The added computation is minimal, since it uses byproducts of the local measurement process. The error-weighting functional provides significantly improved motion field estimates, as measured by motion-compensated interpolation performance. However, the mean-square reconstruction error is somewhat higher than that obtained by performing the much more computationally expensive stochastic optimization.