CNN-type algorithms for H.264 variable block-size partitioning

Authors:
Lauri Koskinen;Ari Paasio;Kari Halonen
Affiliations:
Electronic Circuit Design Laboratory, Helsinki University of Technology, P.O. Box 3000, 02015 TKK, Finland;Microelectronics Laboratory, Department of Information Technology, University of Turku, Finland;Electronic Circuit Design Laboratory, Helsinki University of Technology, P.O. Box 3000, 02015 TKK, Finland
Venue:
Image Communication
Year:
2007

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Abstract

Due to power consumption restrictions, low-power H.264 encoders cannot take advantage of the variable block sizes available in H.264 motion estimation. This work presents two methods to determine a block-size partition without an initial search. With both of these methods, computationally burdensome Lagrange optimization is not required. The methods are derived from a cellular nonlinear network (CNN) segmentation algorithm and, along with the partition, indicate early termination of motion estimation and the skip modes of H.264. Both methods achieve better rate-distortion performance when compared to motion estimation with only 16x16 sized blocks. The 16x16 only case is descriptive of a low-power case where the variable block sizes cannot be used. For low bitrates, both methods achieve equivalent performance when compared to Lagrange optimization. Also presented are the computational complexity of the methods and the power consumption when implemented with existing CNN hardware.