Adaptive parallel video coding algorithm Parallel encoding of video inevitably gives varying frame rate performance due to dynamically changing video content and motion field since the encoding process of each macroblock, especially motion estimation, is data dependent. A multiprocessor schedule optimized for a particular frame with certain macroblock encoding time may not be optimized towards another frame with different encoding time, which causes performance degradation to the parallelization. To tackle this problem, we propose a method based on a batch of nearoptimal schedules generated at compiletime and a runtime mechanism to select the schedule giving the shortest predicted critical path length. This method has the advantage of being nearoptimal using compiletime schedules while involving only runtime selection rather than rescheduling. Implementation on the IBM SP2 multiprocessor system using 24 processors gives an average speedup of about 13.5 (frame rate of 38.5 frames per second) for a CIF sequence consisting of segments of 6 different scenes. This is equivalent to an average improvement of about 16.9% over the single schedule scheme with schedule adapted to each of the scenes. Using an open test sequence consisting of 8 video segments, the average improvement achieved is 13.2%, i.e. an average speedup of 13.3 (35.6 frames per second).

Predictive motion vector field adaptive search technique (PMVFAST)  enhancing block based motion estimation Motion Estimation (ME) is an important part of most video encoding systems, since it could significantly affect the output quality of an encoded sequence. Unfortunately this feature requires a significant part of the encoding time especially when using the straightforward Full Search (FS) algorithm. In this paper a new algorithm is presented named as the Predictive Motion Vector Field Adaptive Search Technique (PMVFAST), which significantly outperforms most if not all other previously proposed algorithms in terms of Speed Up performance. In addition, the output quality of the encoded sequence in terms of PSNR is similar to that of the Full Search algorithm. The proposed algorithm relies mainly upon very robust and reliable predictive techniques and early termination criteria, which make use of parameters adapted to the local characteristics of a frame. Our experiments verify the superiority of the proposed algorithm, not only versus several other wellknown fast algorithms, but also in many cases versus even the Full Search algorithm.