Get The Best of the Three Worlds: Real-Time Neural Image Compression in a Non-GPU Environment

Lossy image compression always faces a tradeoff between rate-distortion performance and compression/decompression speed. With the advent of neural image compression, hardware (GPU) becomes the new vertex in the tradeoff triangle. By resolving the high GPU dependency and improving the low speed of neural models, this paper proposes two non-GPU models that get the best of the three worlds. First, the CPU-friendly Independent Separable Down-Sampling (ISD) and Up-Sampling (ISU) modules are proposed to lighten the network while ensuring a large receptive field. Second, an asymmetric autoencoder architecture is adopted to boost the decoding speed. At last, the Inverse Quantization Residual (IQR) module is proposed to reduce the error caused by quantization. In terms of rate-distortion performance, our network surpasses the state-of-the-art real-time GPU neural compression work at medium and high bit rates. In terms of speed, our model's compression and decompression speeds surpass all other traditional compression methods except JPEG, using only CPUs. In terms of hardware, the proposed models are CPU friendly and perform stably well in a non-GPU environment. The code is publicly available at https://github.com/kengchikengchi/FasiNet.