  Error-bounded lossy compression is a critical technique for significantly
reducing scientific data volumes. Compared to CPU-based compressors, GPU-based
compressors exhibit substantially higher throughputs, fitting better for
today's HPC applications. However, the critical limitations of existing
GPU-based compressors are their low compression ratios and qualities, severely
restricting their applicability. To overcome these, we introduce a new
GPU-based error-bounded scientific lossy compressor named cuSZ-$i$, with the
following contributions: (1) A novel GPU-optimized interpolation-based
prediction method significantly improves the compression ratio and
decompression data quality. (2) The Huffman encoding module in cuSZ-$i$ is
optimized for better efficiency. (3) cuSZ-$i$ is the first to integrate the
NVIDIA Bitcomp-lossless as an additional compression-ratio-enhancing module.
Evaluations show that cuSZ-$i$ significantly outperforms other latest GPU-based
lossy compressors in compression ratio under the same error bound (hence, the
desired quality), showcasing a 476% advantage over the second-best. This leads
to cuSZ-$i$'s optimized performance in several real-world use cases.
