Joint Algorithm-Architecture Optimization of CABAC

This paper uses joint optimization of both the algorithm and architecture to enable high coding efficiency in conjunction with high processing speed and low area cost. Specifically, it presents several optimizations that can be performed on Context Adaptive Binary Arithmetic Coding (CABAC), a form of entropy coding used in H.264/AVC, to achieve the throughput necessary for real-time low power high definition video coding. The combination of syntax element partitions and interleaved entropy slices, referred to as Massively Parallel CABAC, increases the number of binary symbols that can be processing in a cycle. Subinterval reordering is used to reduce the cycle time required to process each binary symbol. Under common conditions using the JM12.0 software, the Massively Parallel CABAC, increases the bins per cycle by 2.7 to 32.8x at a cost of 0.25% to 6.84% coding loss compared with sequential single slice H.264/AVC CABAC. It also provides a 2x reduction in area cost, and reduces memory bandwidth. Subinterval reordering reduces critical path by 14% to 22%, while modifications to context selection reduces memory requirement by 67%. This work illustrates that accounting for implementation cost during video coding algorithms design can enable higher processing speed and reduce hardware cost, while still delivering high coding efficiency in the next generation video coding standard. This work was funded by Texas Instruments. Chip fabrication was provided by Texas Instruments. The work of V. Sze was supported by the Texas Instruments Graduate Women’s Fellowship for Leadership in Microelectronics and NSERC. V. Sze, A. P. Chandrakasan Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA 02139, USA (e-mail: [email protected]; [email protected])