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, while the number of context conditions are decreased by 30%. Moreover, the proposed system outperforms H.264/AVC and JPEG-XR by 8.3% and 4.9%, respectively.

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.

A compact representation scheme is presented for 3-D point data. To describe underlying surface from raw point samples, we dyadically divide a 3-D domain enclosing whole points. Then, local points in each cube are approximated by a plane patch, yielding a multiscale representation of 3-D surface. To reduce the redundancy between different scale models, the geometry innovation is evaluated between different scale planes, which reveals the Euclidian distance between planes. Finally, the geometry innovation coefficients are compressed by a zerotree-based encoder. Based on the multiscale plane representation of 3-D geometry and the efficient plane decomposition method, the proposed scheme provides a desirable framework for 3-D point geometry processing.

We present a multiple description coding (MDC) scheme for 3D plane-based surfaces. First, planes are split into two disjoint subsets, called descriptions, each of which provides an equal contribution in 3D surface reconstruction. To optimize the quality of the decoded surface, planes in each description are adaptively compressed according to the channel error condition. Then, the two compressed bitstreams are transmitted over distinct channels to the decoder. At the decoder, if both channels are available, the two bitstreams are decoded and merged together to reconstruct a high quality surface. If only one channel is available, we employ a hole filling method to fill visual holes and reconstruct a smooth 3D surface. Therefore, the proposed algorithm provides an acceptable 3D surface, even when one channel is totally lost.

In this paper, we present a novel geometry compression scheme for compactly representing 3-D sampled data. The proposed framework is based on the octree structured multiscale geometry representation, where local surface is expressed by multiscale plane descriptions. To reduce planar redundancy between different scale plane approximations in the multiscale pyramid, the multiscale plane pyramid is decomposed and the resulting refinement pyramid is encoded using the zerotree based coding method. The decomposition captures the geometry innovation to refine coarse geometry. Moreover, the zerotree based coding method offers the ordered bitstream according to the contribution for reconstructing the geometry innovation. Therefore, we demonstrate that the proposed algorithm compactly represents the 3-D point samples yielding progressive refinement of the surface geometry.

An error resilient three-dimensional (3-D) mesh coding system is proposed in this paper. The encoder uses a shape adaptive data partitioning scheme to alleviate the effect of error propagation. An input mesh surface is coarsely divided into smooth and detailed regions, and each region is further divided into partitions of similar sizes. Then, those partitions are progressively compressed and their joint boundaries are compressed using the boundary edge collapse rule. At the decoder, the boundary edge collapse rule facilitates the seamless assembly of the partitions. When no data is available for a partition due to transmission errors, we employ a concealment scheme based on the projection onto convex sets (POCS) theory. Simulation results demonstrate that the proposed algorithm reconstructs 3-D mesh surfaces faithfully even in severe error prone environments

Compressed bit streams of 3D mesh data are highly vulnerable to transmission errors due to predictive coding and entropy coding. In this work, we propose an error resilient and progressive coding scheme for 3D meshes. To make a compressed bit stream robust to transmission errors, 3D mesh data is partitioned into several segments, which are then encoded independently. In the decoder, a novel error concealment scheme is employed to improve the visual quality of corrupted segments. Simulation results show that the proposed algorithm provides a good quality reconstruction even in severe error conditions.

In this paper, we propose a progressive compression algorithm for three-dimensional (3D) mesh data, which can efficiently encode the connectivity and geometry information. Using cosine index predictor and vertex confinement rule, the proposed scheme encodes the connectivity information compactly. For the geometry-information coding, a 2-stage geometry predictor is proposed to reduce the prediction error more effectively than the conventional techniques. Simulation results show that the proposed algorithm yields better coding performance than the conventional algorithm.