Dai-woong Choi

Novel context modeling scheme for lossless image compression using statistical reference

A novel context modeling scheme is presented for loss-less image compression. First, each line in the input image is divided into 1 times N line segments, called processing unit (PU). Then, the statistical reference is evaluated in each PU, which reveals the randomness of pixels in the local image region. The context is designed based on both neighbor pixels and the statistical reference. Finally, each pixel is adaptively compressed based on the proposed context condition. In the experiment, the proposed scheme yields the comparable performance to the standard JPEG-LS , while the number of context conditions are decreased by 30%, Moreover, the compression performance of the proposed system shows slightly better performance than the new image compression standard of JPEG-XR.

Lossless compound image compression for digital imaging system

In this paper, we propose a simple but efficient lossless compression scheme for compound images. For natural image region in compound image, we introduce an efficient context modeling scheme, with reduced complexity in context modeling. Then, a block skip method is adopted to effectively express homogeneous regions. We also incorporate a pattern run-based representation for graphic regions. In the experiment, the proposed scheme outperforms JPEG-LS [1] and JPEG-XR [2] by 1.4 times and 2.9 times, respectively.

Low complexity lossless image compression using efficient context modeling

A novel context modeling scheme is presented for lossless image compression. First, each line in the input image is divided into 1 × N line segments, called processing unit (PU). Then, the statistical reference is evaluated in each PU, which reveals the randomness of pixels in the local image region. The context is designed based on both neighbor pixels and the statistical reference. Finally, each pixel is adaptively compressed based on the proposed context condition. In the experiment, the proposed scheme yields the comparable performance to the standard JPEG-LS [1], while the number of context conditions are decreased by 30%. Moreover, the proposed system outperforms H.264/AVC [2] and JPEG-XR [3] by 8.3% and 4.9%, respectively.

Novel context template design scheme for lossless color halftone image compression

A novel template design algorithm is presented for lossless compression of color halftone images. First, we extract the line pattern in the neighbor local region of each bi-level pixel. Then, the representative line pattern is evaluated from these obtained lines, using the least square error minimization. According to the evaluated line pattern and two design constraints, therefore, the context template is shaped. With the designed template, finally, each color channel image is compressed by a context-based binary arithmetic encoder. Based on the adaptiveness of the template to the input image, the proposed templates yield better compression performance than the conventional JBIG templates, which saves 35% of the JBIG bitstream.

Lossless image compression using valley transform

In this paper, a novel geometric transform, called the valley transform (VT), is proposed for robust compression of noisy images. The VT employs a nonlinear transform to convert random signals into regular ones. The proposed VT is able to change random pixels to regular one. For testing the compression performance, the VT is combined with the SPHIT method. The VT yields slightly better performance than the conventional image codecs such as JBIG, HD-photo and H.264 intra method in the lower bit-plane image compression.