Spherical Image Compression Using Spherical Wavelet Transform

The Spherical Measure Based Spherical Image Representation (SMSIR) has nearly uniformly distributed pixels in the spherical domain with effective index schemes. Based on SMSIR, the spherical wavelet transform can be efficiently designed, which can capture the spherical geometry feature in a compact manner and provides a powerful tool for spherical image compression. In this paper, we propose an efficient compression scheme for SMSIR images named Spherical Set Partitioning in Hierarchical Trees (S-SPIHT) using the spherical wavelet transform, which exploits the inherent similarities across the subbands in the spherical wavelet decomposition of a SMSIR image. The proposed S-SPIHT can progressively transform spherical wavelet coefficients into bit-stream, and generate an embedded compressed bit-stream that can be efficiently decoded at several spherical image quality levels. The most crucial part of our proposed S-SPIHT is the redesign of scanning the wavelet coefficients corresponding to different index schemes. We design three scanning methods, namely ordered root tree index scanning (ORTIS), dyadic index progressive scanning(DIPS) and dyadic index cross scanning(DICS)to efficiently reorganize the wavelet coefficients. These methods can effectively exploit the self-similarity between sub-bands and the fact that the high-frequency sub-bands mostly contain insignificant coefficients. Experimental results on widely-used datasets demonstrate that our proposed S-SPIHT outperforms the straightforward SPIHT for SMSIR images in terms of PSNR, S-PSNR and SSIM.