GPU-accelerated Lossless Image Compression with Massive Parallelization

Yu Shen, Gang Wu, Vishy Swaminathan, Haoliang Wang, Stefano Petrangeli, Tong Yu

Abstract: With the rapid increase of digital content like images or videos nowadays, compression technology contributes more to saving storage or transferring time with large-scale data. While some existing methods already achieved a great compression ratio, they are not applicable to certain live applications under low efficiency. In this work, we use massive parallelization to speed up the SOTA baseline FLIF, including bitwise-equivalent speedup and learning-based speedup. Our method achieves $38.7 \times$ throughputs for encoding and $2.45 \times$ throughputs for decoding, compared to the baseline FLIF.