Depth Buffer Compression for Stochastic Motion Blur Rasterization

Abstract

Previous depth buffer compression schemes are tuned for compressing depths values generated when rasterizing static triangles. They provide generous bandwidth usage savings, and are of great importance to graphics processors. However, stochastic rasterizationfor motion blur and depth of field is becoming a reality even for real-time graphics, and previous depth buffer compression algorithms fail to compress such buffers due to the irregularity of the positions and depths of the rendered samples. Therefore, we presenta new algorithm that targets compression of scenes rendered with stochastic motion blur rasterization. If possible, our algorithm fits a single time-dependent predictor function for all the samples in a tile. However, sometimes the depths are localized in more than onelayer, and we therefore apply a clustering algorithm to split the tile of samples into two layers. One time-dependent predictor function is then created per layer. The residuals between the predictor and the actual depths are then stored as delta corrections. For scenes with moderate motion, our algorithm can compress down to 65% compared to 75% for the previously best algorithm for stochastic buffers.