Lossless Frame Memory Compression Using Pixel-Grain Prediction and Dynamic Order Entropy Coding

Abstract

Power constraints constitute a critical design issue for the portable video codec system, in which the external dynamic random access memory (DRAM) accounts for more than half of the overall system power requirements. With the ultrahigh-definition video specifications, the power consumed by accessing reference frames in the external DRAM has become the bottleneck for the portable video encoding system design. To relieve the dynamic power stresses introduced by the DRAM, a lossless compression algorithm is devised to reduce the external traffic and the memory requirements of reference frames. First, pixel-granularity directional prediction is adopted to decrease the prediction residual energy by 54.1% over the previous horizontal prediction. Second, the dynamic $k$ th-order unary/Exp-Golomb rice coding is applied to accommodate the large-valued prediction residues. With the aforementioned techniques, an average data traffic reduction of 68.5% for the off-chip reference frames is obtained, which consequently reduces the dynamic power requirements of the DRAM by 42.3%. Based on the high data reduction ratio of the proposed compression algorithm, a partition group table-based storage space reduction scheme is provided to improve the utilization of row buffers in the DRAM. Consequently, an additional 14.5% of the DRAM dynamic power can be saved by reducing the number of row buffer activations. In total, a 56.8% decrease in the dynamic power requirements of the external reference frame access can be obtained using our strategies. With TSMC 65-nm CMOS logic technology, our algorithm was implemented in a parallel VLSI architecture based on a compressor and decompressor at the cost of 36.5k and 34.7k, respectively, in terms of gate count. The throughputs of the proposed compressor and decompressor are 1.54 and 0.78 Gpixels/s, which are suitable for quad full high definition (4K) @ 94 frames/s real-time encoding with the level-D reference data reuse scheme.