Chuo-Ling Chang

Direction-Adaptive Discrete Wavelet Transform for Image Compression

We propose a direction-adaptive DWT (DA-DWT) that locally adapts the filtering directions to image content based on directional lifting. With the adaptive transform, energy compaction is improved for sharp image features. A mathematical analysis based on an anisotropic statistical image model is presented to quantify the theoretical gain achieved by adapting the filtering directions. The analysis indicates that the proposed DA-DWT is more effective than other lifting-based approaches. Experimental results report a gain of up to 2.5 dB in PSNR over the conventional DWT for typical test images. Subjectively, the reconstruction from the DA-DWT better represents the structure in the image and is visually more pleasing

Light field compression using disparity-compensated lifting and shape adaptation

We propose disparity-compensated lifting for wavelet compression of light fields. With this approach, we obtain the benefits of wavelet coding, such as scalability in all dimensions, as well as superior compression performance. Additionally, the proposed approach solves the irreversibility limitations of previous light field wavelet coding approaches, using the lifting structure. Our scheme incorporates disparity compensation into the lifting structure for the transform across the views in the light field data set. Another transform is performed to exploit the coherence among neighboring pixels, followed by a modified SPIHT coder and rate-distortion optimized bitstream assembly. A view-sequencing algorithm is developed to organize the views for encoding. For light fields of an object, we propose to use shape adaptation to improve the compression efficiency and visual quality of the images. The necessary shape information is efficiently coded based on prediction from the existing geometry model. Experimental results show that the proposed scheme exhibits superior compression performance over existing light field compression techniques.

Light field compression using disparity-compensated lifting

We propose a novel approach for light field compression that incorporates disparity compensation into 4-D wavelet coding using disparity-compensated lifting. With this approach, we obtain the benefits of wavelet coding, including compression efficiency and scalability in all dimensions. Additionally, our proposed approach solves the irreversibility limitations of previous wavelet coding approaches. Experimental results show that the compression efficiency of the proposed technique outperforms current state-of-the-art wavelet coding techniques by a wide margin.

Adaptive Wavelet Transform for Image Compression via Directional Quincunx Lifting

We propose a novel adaptive wavelet transform that exploits local image properties for image compression. It combines wavelet filters adaptive to edge orientations with quincunx subsampling to form a 2-D nonseparable transform through lifting. Filter selections are efficiently represented. Significant improvement on both subjective and objective quality over the conventional separable transform is observed. In addition, unlike previous adaptive transforms, the symmetry in quincunx subsampling enables even quality for image features along different directions and the compression performance is insensitive to image orientation

Direction-adaptive partitioned block transform for image coding

We propose a direction-adaptive block transform that exploits the directionality in images to improve the compression performance. It can be viewed as a generalization of variable block-size transforms with the inclusion of non-rectangular partitions and directional transforms. As a block transform, it can be directly combined with block-wise intra and inter prediction in video coding standards. Experimental results show that the proposed transform outperforms the variable block-size 2-D DCT by up to 3 dB. It also outperforms a previously proposed direction-adaptive DCT by up to 2 dB. When combined with intra prediction, the proposed transform further provides a gain of 0.5 to 2 dB upon intra prediction with the 2-D DCT.

Compression of image patches for local feature extraction

Local features are widely used for content-based image retrieval and object recognition. We present an efficient method for encoding digital images suitable for local feature extraction. First, we find the patches in the image corresponding to the detected features. Then, we extract these patches at their characteristic scale and orientation and encode them for efficient transmission. A Discrete Cosine Transform (DCT) with adaptive block size is used for patch compression. We compare this method to directly compressing feature descriptors using transform coding. Experimental results show the superior performance of our technique. Image patches can be compressed to rates around 55 bits/patch (18x compression relative to uncompressed SIFT feature descriptors) and still achieve good image matching performance.

Direction-Adaptive Discrete Wavelet Transform via Directional Lifting and Bandeletization

We propose an adaptive lifted discrete wavelet transform to locally adapt the filtering direction to the geometric flow in the image. The proposed approach refines previous directional lifting approaches to achieve superior compression performance with less demand for computation. Additionally, a bandeletization procedure is combined with directional lifting in a unified framework to further remove the correlation in the wavelet coefficients. Up to 2.8 dB improvement in PSNR over the conventional 2-D CDF 9/7 wavelet transform for natural images is reported. Significant improvement in subjective quality is also observed.

Inter-view wavelet compression of light fields with disparity-compensated lifting

We propose a novel approach that uses disparity-compensated lifting for wavelet compression of light fields. Disparity compensation is incorporated into the lifting structure for the transform across the views to solve the irreversibility limitation in previous wavelet coding schemes. With this approach, we obtain the benefits of wavelet coding, such as scalability in all dimensions, as well as superior compression performance. For light fields of an object, shape adaptation is adopted to improve the compression efficiency and visual quality of reconstructed images. In this work we extend the scheme to handle light fields with arbitrary camera arrangements. A view-sequencing algorithm is developed to encode the images. Experimental results show that the proposed scheme outperforms existing light field compression techniques in terms of compression efficiency and visual quality of the reconstructed views.

Direction-Adaptive Partitioned Block Transform for Color Image Coding

The direction-adaptive partitioned block transform (DA-PBT) is proposed to exploit the directional features in color images to improve coding performance. Depending on the directionality in an image block, the transform either selects one of the eight directional modes or falls back to the nondirectional mode equivalent to the conventional 2-D DCT. The selection of a directional mode determines the transform direction that provides directional basis functions, the block partitioning that spatially confines the high-frequency energy, the scanning order that arranges the transform coefficients into a 1-D sequence for efficient entropy coding, and the quantization matrix optimized for visual quality. The DA-PBT can be incorporated into image coding using a rate-distortion optimized framework for direction selection, and can therefore be viewed as a generalization of variable blocksize transforms with the inclusion of directional transforms and nonrectangular partitions. As a block transform, it can naturally be combined with block-based intra or inter prediction to exploit the directionality remaining in the residual. Experimental results show that the proposed DA-PBT outperforms the 2-D DCT by more than 2 dB for test images with directional features. It also greatly reduces the ringing and checkerboard artifacts typically observed around directional features in images. The DA-PBT also consistently outperforms a previously proposed directional DCT. When combined with directional prediction, gains are less than additive, as similar signal properties are exploited by the prediction and the transform. For hybrid video coding, significant gains are shown for intra coding, but not for encoding the residual after accurate motion-compensated prediction.

Shape adaptation for light field compression

We propose a method of using shape adaptation for compression of light fields of 3D objects. Shape adaptation is incorporated into two light field coders, both applying disparity compensation with an explicit geometry model, to improve the compression efficiency. The shape information can be derived at the decoder from an accurate geometry model. If the available geometry is inaccurate, we propose to code the exact 2D shapes with the aid of the approximate geometry. Experiments show that shape adaptation greatly improves the compression performance of both coders.