MaskTac: Mask-guided Learned Lossy Compression for Sparse Tactile Glove Data

Tactile sensing provides direct feedback about physical contact for a wide range of perception and interaction applications. However, high-quality force-based tactile datasets remain limited. The widespread use of tactile data also creates a growing need of efficient compression for storage and transmission. To mitagate these gaps, we firstly introduce TacGlove, a tactile glove dataset collected using a customized wearable tactile glove. It contains 4,660 sequences and 781,969 frames, covering 32 object classes and 8 grasp types. The glove records three-axis contact forces from 1,207 taxels distributed across the hand. Such force-based tactile glove data exhibit pronounced spatiotemporal sparsity and strong correlations within contact regions. Based on these characteristics, we then propose MaskTac, a learned lossy codec for sparse tactile glove data. By combining sparsity-aware quantization with a mask-guided contextual entropy model, MaskTac exploits data spatiotemporal sparsity and reduces spatiotemporal redundancy within contact regions for efficient compression. We further establish a benchmark for tactile compression by adapting traditional and learned visual codecs to TacGlove. The benchmark evaluates both reconstruction fidelity and object and grasp classification accuracy. Experiments show that MaskTac outperforms the benchmarked codecs, achieving BD-rate improvement relative to VTM of 34.04% under PSNR, 32.57% under MS-SSIM, and 58.83% under Contact F1. Ablation studies further validate the effectiveness of the proposed design.