Accelerated implementation of adaptive directional lifting-based discrete wavelet transform on GPU

Because of the high data dependency between ADL (adaptive directional lifting) operations, such as interpolation, directional prediction and update, the existing CUDA-specific (Compute Unified Device Architecture) implementation of traditional rectilinear lifting-based transform is difficult to be used for ADL-based transform. This paper proposes a novel CUDA-specific method named Slice for implementation of the ADL-based wavelet transforms on GPU (Graphics Processing Unit). Compared with the previous CUDA-specific methods the proposed method makes each step handled by a different kernel to avoid unnecessary waiting time between lifting steps. Meanwhile the interpolation and decomposition including prediction and update are executed in an interleaving style for each filtered pixel. Moreover, the coalesced memory accesses are exploited to the greatest extent by coalesced reading a slice of data to the shared memory and coalesced writing them back to the global memory after being processed. The results show that the Slice method overcomes the limitation of high data dependency between the lifting steps and achieves more than 10 times speedup compared to the optimized CPU implementation for the ADL-based transform.