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.

翻译：与传统方法相比,PSNR和MS-SSIM图像压缩在PSNR和MS-SSIM中取得了超常的扭曲率性能。然而,它却受到密集计算的影响,对于现实应用来说,这是不可容忍的,并且导致其目前有限的工业应用。在本文中,我们引入神经结构搜索(NAS),以设计更高效的网络,保持较低的延缓性,并利用量化来加速推导过程。与此同时,在多读和SIMD等工程方面作出了努力,以提高效率。此外,我们利用PSNR和MS-SSIM的混合损失来优化PSIM的混合性能,我们获得的MS-SSIM比JPEG、JEG XL和AVIF的所有比率都高得多,而PSNR则比JPEG XL和AVIF高得多。我们的L软件实施速度比jpeg-turbo要快得多,而比JEGXL和AVIF要快很多倍。 此外,我们实施LL的TRTP在10GPU的可比较的图像上达到145页和208。