Abstract-We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios.Index Terms-Compound image compression, compound image segmentation, palette-based coding, shape-based coding, shape primitive extraction.

In this paper, we presented a method for integer reversible implementation of KLT for multiple component image compression. The progressive-to-lossless compression algorithm employed the JPEG-2000 transform coding strategy using the multiple component transform (MCT) across the components, followed by a 2-dimensional wavelet transform on individual eigen images. The linear MCTs we tested and compared are KLT, discrete wavelet transform (DWT), and a tasselled cap transform (TCT) for TM satellite images only. The computational complexity of the reversible integer implementation is no more than that of naïve transformation, and the overhead data is very small. Its effectiveness was evaluated using two 6-band Landsat TM satellite images and an 80-component hyper-spectral remotely-sensed image. Experiments with KLT and wavelet based JPEG-2000 show that reversible KLT (RKLT) outperforms other approaches for all of the test images in the case of both lossy and lossless compression.

A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the luminance component at each pixel, and thus, avoids the associated estimation error. Instead, we directly use the acquired CFA color intensity at each pixel as an input component. Two independent chrominance components are estimated at each pixel based on the inter-pixel chrominance in the window, which is captured with the difference of CFA color values between the pixel of interest and its neighbors. Two mechanisms are employed for the accurate estimation: distance-related and edge-sensing weighting to reflect the confidence levels of the inter-pixel chrominance components, and pseudoinverse-based estimation from the components in a window. Then from the acquired CFA color component and two estimated chrominance components, the three primary colors are reconstructed by a linear color transform, which is optimized for the least transform error. Our experiments show that the proposed method is much better than other published universal demosaicking methods.