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Summary form only given. Large aperture static imaging spectrometer (LASIS) is a new kind of interferometer spectrometer with the advantages of high throughput and large field of view. The LASIS data contains spatial information in principle component along spatial direction and spectral information in modulated component along Optical Path Difference (OPD) direction. LASIS data have clearly parallel displacement of spatial information among continuous frames. It is a unique characteristic for LASIS data compared with other kinds of interferometer spectrometer such as SMII data and GITFS data. Although dispersive spectrometer, such as AVIRIS data, also contains some spatial information, each frame in a group shows the same ground view with different wavelengths spectrum without any motion displacement among inter frames. Based on these characteristics, we propose a lossless data compression method named Dual-direction Prediction Vector Quantization (DPVQ). With a dual-direction prediction on both spatial and spectral direction, redundancy in LASIS data is largely removed by minimizing the prediction residue in DPVQ. Then a fast vector quantization (VQ) avoiding codebook splitting process is applied after prediction. Considering time efficiency, the dual-direction prediction and VQ in DPVQ are optimized to reduce the calculations, so that optimized prediction saves 60% running time and fast VQ saves about 25% running time with a similar quantization quality compared with classical generalized Lloyd algorithm (GLA). Experimental results, shown in Fig. 2, indicate that DPVQ can achieve a maximal Compression Ratio (CR) at about 3.4, which outperforms many existing lossless compression algorithms.