Title :
Improved CNN algorithm for H.264 motion estimation partitions
Author :
Koskinen, Lauri ; Halonen, Kari ; Paasio, Ari
Author_Institution :
Electron. Circuit Design Lab., Helsinki Univ. of Technol., Finland
Abstract :
To take full advantage of the motion estimation of the new video coding standard H.264, low-power video encoders will need specific hardware accelerators. Presented here is an improved partitioning method to decrease the computational load of variable block size motion estimation. The two-step partitioning method improves on a previous three-step method. The method is derived from a cellular nonlinear network (CNN) segmentation algorithm and, along with the partition, indicates early termination of motion estimation and the skip modes of H.264. The algorithm achieves better rate-distortion performance when compared to motion estimation with only 16×16 sized blocks and, for low bit rates, equivalent performance when compared to Lagrange optimization.
Keywords :
cellular neural nets; image segmentation; motion estimation; video coding; CNN segmentation algorithm; H.264 motion estimation partition; cellular nonlinear network; low-power video encoders; partitioning method; video coding standard; Analog computers; Cameras; Cellular neural networks; Hardware; Lagrangian functions; Mobile computing; Motion estimation; Partitioning algorithms; Sensor arrays; Video coding;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Print_ISBN :
0-7803-9185-3
DOI :
10.1109/CNNA.2005.1543181