In this work, near-lossless compression yielding strictly bounded reconstruction error is proposed for high-quality compression of remote sensing images. A classified causal DPCM scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-lossless) compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually lossless even though encoding was not strictly reversible.

Near-lossless compression of 3-D optical data

According to the correlated characteristic of remotely sensed multispectral images (RSMI) in the spectral and spatial
domains, an effective and lossy YCrCb+IWT compression algorithm is proposed. The algorithm combines YCrCb
transform with integer wavelet transform (IWT) to compress data, and data redundance of spectral and spatial domains is
removed respectively. The important degree of the each subband is determined according to the energy of the each
subband. Furthermore, each subband is quantified using adaptive threshold according to their important degree, then
fixed bit-plane coding and Run Length Encoding are individually used to the quantified data of every subband and
important graph. When implementing compression algorithm, in order to ensure better quality of reconstructed image,
the compression with little distortion is utilized for luminance information Y. Simultaneously, in order to obtain higher
compression ratio, the compression with biggish distortion is carried out for chrominance information Cr and Cb. The
simulation experiment indicates that this algorithm can receive good compression performance of average CR≥ 7 and
average PSNR ≥ 33dB for RSMI of different content and texture. In addition, the algorithm requires small storage and is
easy to be realized in hardware, so it is suitable for space-borne application.

Bao-feng Tian

Papers

Lossy compression algorithm of remotely sensed multispectral images based on YCrCb transform and IWT