This paper presents the design and implementation of a dedicated hardware architecture for binary arithmetic decoder (BAD) engines of CABAD, as defined in the H.264/AVC video compression standard. The BAD is the most important CABAD process, which is the main entropy encoding method defined by the H.264/AVC standard. The BAD is composed by tree engines: Regular, Bypass and Terminate. A large set of software experiments was made to profile each engine. Based on bitstream flow analysis a new dedicated hardware architecture was proposed to improve the hardware efficiency of BAD engines. The proposed solution was described in VHDL and synthesized to a Xilinx Virtex2-Pro FPGA. The results show that the developed architecture reaches 103 MHz, and delivers up to 4 bins per cycle in bypass engines, against 2 bins per cycle as exposed in the literature.
The design and implementation of a hardware accelerator dedicated to Binary Arithmetic Decoding Engine (BADE) is presented. This is the main module of the Context-Adaptive Binary Arithmetic Decoder (CABAD), as used in the H.264/AVC on-chip video decoders. We propose and implement a new approach for accelerating the decoding hardware of the significance map by providing the correct context for the regular hardware engine of the (CABAD). The design development was based on a large set of software experiments, which aimed at exploiting the characteristic behavior of the bitstream during decoding. The analysis gave new insights to propose a new hardware architecture to improve throughput of regular engines for significance map with low silicon area overhead. The proposed solution was described in VHDL and synthesized to standard cells in IBM 0.18 μm CMOS process. The results show that the developed architecture reaches 187 MHz with a non optimized physical synthesis.
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