  Learned image compression has achieved extraordinary rate-distortion
performance in PSNR and MS-SSIM compared to traditional methods. However, it
suffers from intensive computation, which is intolerable for real-world
applications and leads to its limited industrial application for now. In this
paper, we introduce neural architecture search (NAS) to designing more
efficient networks with lower latency, and leverage quantization to accelerate
the inference process. Meanwhile, efforts in engineering like multi-threading
and SIMD have been made to improve efficiency. Optimized using a hybrid loss of
PSNR and MS-SSIM for better visual quality, we obtain much higher MS-SSIM than
JPEG, JPEG XL and AVIF over all bit rates, and PSNR between that of JPEG XL and
AVIF. Our software implementation of LIC achieves comparable or even faster
inference speed compared to jpeg-turbo while being multiple times faster than
JPEG XL and AVIF. Besides, our implementation of LIC reaches stunning
throughput of 145 fps for encoding and 208 fps for decoding on a Tesla T4 GPU
for 1080p images. On CPU, the latency of our implementation is comparable with
JPEG XL.
