Wai-Kuen Cham

Postprocessing of Low Bit-Rate Block DCT Coded Images Based on a Fields of Experts Prior

Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we propose to solve it by the maximum a posteriori criterion. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field based on the fields of experts framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality. In addition, we examine the noise model used and its parameter setting. The noise model assumes that the DCT coefficients and their quantization errors are independent. This assumption is no longer valid when the coefficients are truncated. We explain how this problem can be rectified using the current parameter setting.

Gradient-Directed Multiexposure Composition

In this paper, we present a simple yet effective method that takes advantage of the gradient information to accomplish the multiexposure image composition in both static and dynamic scenes. Given multiple images with different exposures, the proposed approach is capable of producing a pleasant tone-mapped-like high-dynamic-range image by compositing them seamlessly with the guidance of gradient-based quality assessment. In particular, two novel quality measures, namely, visibility and consistency, are developed based on the observations of gradient changes among different exposures. Experiments in various static and dynamic scenes are conducted to demonstrate the effectiveness of the proposed method.

Performance Evaluation of Full Search Equivalent Pattern Matching Algorithms

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations.

Fast Algorithm for Walsh Hadamard Transform on Sliding Windows

This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing fast algorithms for Walsh Hadamard Transform on sliding windows.

Hallucinating Face in the DCT Domain

In this paper, we propose a novel learning-based face hallucination framework built in the DCT domain, which can produce a high-resolution face image from a single low-resolution one. The problem is formulated as inferring the DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Our study shows that DC coefficients can be estimated fairly accurately by simple interpolation-based methods. AC coefficients, which contain the information of local features of face image, cannot be estimated well using interpolation. A simple but effective learning-based inference model is proposed to infer the ac coefficients. Experiments have been conducted to demonstrate the effectiveness of the proposed method in producing high quality hallucinated face images.

Gradient-directed composition of multi-exposure images

In this paper, we present a simple yet effective method that takes advantage of the gradient information to accomplish the multi-exposure image composition in both static and dynamic scenes. Given multiple images with different exposures, the proposed approach is capable of producing a pleasant tonemapped-like high dynamic range (HDR) image by compositing them seamlessly with the guidance of gradient-based quality assessment. Especially, two novel quality measures: visibility and consistency, are developed based on the observations of gradient changes among different exposures. Experiments in various static and dynamic scenes are conducted to demonstrate the effectiveness of the proposed method.

Single-Image Refocusing and Defocusing

In this paper, we present a postprocessing method to tackle the single-image refocusing-and-defocusing problem. The proposed method can accomplish the tasks of focus-map estimation and image refocusing and defocusing. Given an image with a mixture of focused and defocused objects, we first detect the edges and then estimate the focus map based on the edge blurriness, which is depicted explicitly by a parametric model. The image refocusing problem is addressed in a blind deconvolution framework, where the image prior is modeled by using both global and local constraints. In particular, we correct the defocused blurry edges to sharp ones with the aid of the parametric edge model and then render this cue as a local prior to ensure the sharpness of the refocused image. Experimental results demonstrate that the proposed method performs well in producing visually plausible images with different focus effects from a single input.

2-D Order-16 Integer Transforms for HD Video Coding

In this paper, the spatial properties of high-definition (HD) videos are investigated based on a large set of HD video sequences. Compared with lower resolution videos, the prediction errors of HD videos have higher correlation. Hence, we propose using 2-D order-16 transforms for HD video coding, which are expected to be more efficient to exploit this spatial property, and specifically propose two types of 2-D order-16 integer transforms, nonorthogonal integer cosine transform (ICT) and modified ICT. The former resembles the discrete cosine transform (DCT) and is approximately orthogonal, of which the transform error introduced by the nonorthogonality is proven to be negligible. The latter modifies the structure of the DCT matrix and is inherently orthogonal, no matter what the values of the matrix elements are. Both types allow selecting matrix elements more freely by releasing the orthogonality constraint and can provide comparable performance with that of the DCT. Each type is integrated into the audio and video coding standard (AVS) Enhanced Profile (EP) and the H.264 high profile (HP), respectively, and used adaptively as an alternative to the 2-D order-8 transform according to local activities. At the same time, many efforts have been devoted to further reducing the complexity of the 2-D order-16 transforms and specially for the modified ICT, a fast algorithm is developed and extended to a universal approach. Experimental results show that 2-D order-16 transforms provide significant performance improvement for both AVS enhanced profile and H.264 high profile, which means they can be efficient coding tools especially for HD video coding.

Model-based edge reconstruction for low bit-rate wavelet-compressed images

At low bit rates, wavelet-based image coding is superior to most traditional block-based methods in terms of visibility and severity of coding artifacts in coded images. However, the compressed images still suffer from obvious distortions around sharp edges, which are perceptually objectionable. In order to improve the image quality for low bit-rate wavelet-based image coding, we proposed a model-based edge-reconstruction algorithm for recovering the lossy edges in coded images. Our approach applies a general model to represent varieties of edges existing in an image. Based on this model, the edge degradation process due to quantization errors of wavelet coefficients is analyzed with the characterization of two kinds of artifacts at edges. We develop two operations, model-based edge approximation and Gaussian smoothing, to reconstruct distorted edges by reducing both artifacts respectively. The proposed method is able to improve the image quality in terms of both visual perception and image fidelity (peak signal-to-noise ratio) for most images coded by wavelet-based methods at low bit-rates.

An order-16 integer cosine transform

It is shown that it is possible to replace the real-numbered elements of a discrete cosine transform (DCT) matrix with integers and still maintain the structure, i.e., relative magnitudes and orthogonality, among the matrix elements. The result is an integer cosine transform (ICT). Thirteen ICTs have been found, and some of them have performance comparable to the DCT. The main advantage of the ICT lies in having only integer values, which in two cases can be represented perfectly by 6-bit numbers, thus providing a potential reduction in the computational complexity. >