Chengjie Tu

Lapped transform via time-domain pre- and post-filtering

This paper presents a general framework of constructing a large family of lapped transforms with symmetric basis functions by adding simple time-domain pre- and post-processing modules onto existing block discrete cosine transform (DCT)-based infrastructures. A subset of the resulting solutions is closed-form, fast computable, modular, near optimal in the energy compaction sense and leads to an elegant boundary handling of finite-length data. Starting from these solutions, a general framework for block-based signal decomposition with a high degree of flexibility and adaptivity is developed. Several simplified models are also introduced to approximate the optimal solutions. These models are based on cascades of plane rotation operators and lifting steps, respectively. Despite tremendous savings in computational complexity, the optimized results of these simplified models are virtually identical to that of the complete solution. The multiplierless versions of these pre- and post-filters when combined with an appropriate multiplierless block transform, such as the binDCT, which is described in an earlier paper by Liang and Tran (see IEEE Trans. Signal Processing, vol.49, p.3032-44, Dec. 2001), generate a family of very large scale integration (VLSI)-friendly fast lapped transforms with reversible integer-to-integer mapping. Numerous design examples with arbitrary number of channels and arbitrary number of borrowed samples are presented.

Context-based entropy coding of block transform coefficients for image compression

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. Wavelet-based JPEG2000 is emerging as the new high-performance international standard for still image compression. An often asked question is: how much of the coding improvement is due to the transform and how much is due to the encoding strategy? Current block transform coders such as JPEG suffer from poor context modeling and fail to take full advantage of correlation in both space and frequency sense. This paper presents a simple, fast, and efficient adaptive block transform image coding algorithm based on a combination of prefiltering, postfiltering, and high-order space-frequency context modeling of block transform coefficients. Despite the simplicity constraints, coding results show that the proposed coder achieves competitive R-D performance compared to the best wavelet coders in the literature.

Adaptive runlength coding

Runlength coding is the standard coding technique for block transform-based image/video compression. A block of quantized transform coefficients is first represented as a sequence of RUN/LEVEL pairs that are then entropy coded-RUN being the number of consecutive zeros and LEVEL being the value of the following nonzero coefficient. We point out the inefficiency of conventional runlength coding and introduce a novel adaptive runlength (ARL) coding scheme that encodes RUN and LEVEL separately using adaptive binary arithmetic coding and simple context modeling. We aim to maximize compression efficiency by adaptively exploiting the characteristics of block transform coefficients and the dependency between RUN and LEVEL. Coding results show that with the same level of complexity, the proposed ARL coding algorithm outperforms the conventional runlength coding scheme by a large margin in the rate-distortion sense.

Multiple Description Coding With Prediction Compensation

A new multiple description coding paradigm is proposed by combining the time-domain lapped transform, block level source splitting, linear prediction, and prediction residual encoding. The method provides effective redundancy control and fully utilizes the source correlation. The joint optimization of all system components and the asymptotic performance analysis are presented. Image coding results demonstrate the superior performance of the proposed method, especially at low redundancies.

Error resilient pre/post-filtering for DCT-based block coding systems

Block coding based on the discrete cosine transform (DCT) is very popular in image and video compression. Pre/post-filtering can be attached to a DCT-based block coding system to improve coding efficiency as well as to mitigate blocking artifacts. Previously designed pre/post-filters are optimized to maximize coding efficiency solely. For image and video communication over unreliable channels, those pre/post-filters are sensitive to transmission errors. This paper addresses the problem of designing pre/post-filters which are more error resilient. Reconstruction performance is measured by how low the average reconstruction error is, and how uniformly the reconstruction error is distributed. A family of pre/post-filters is designed to provide desired tradeoffs between coding efficiency and robustness to transmission errors. Experiments show that these filtering operators can achieve superior reconstruction performance without sacrificing much coding performance.

Lapped-transform-based video coding

Standard video compression algorithms share one common coding framework based on the hybrid block-based motion-compensated DCT structure. Recently, video compression at low bit-rates has become the focus of research in the signal processing community due to the expanding applications in video conferencing, video over telephone lines, streaming video over the Internet, multimedia and wireless video communications. Unfortunately, in these situations, the notorious blocking artifacts resulting from block DCT and block-based motion estimation/compensation set a severe limit on the achievable bit-rate with acceptable quality. To avoid blocking artifacts and to improve video coding efficiency, this paper presents a novel video compression algorithm based on the lapped transform and overlapping block motion estimation/compensation. Our long-term focus is to develop a complete integrated framework for lapped-transform-based video coding, ranging from theory, design, fast implementations, to practically desirable features for video streaming and delivery over communication networks. In this paper, we are mainly concerned with fundamental theoretical issues. The goals of the proposed video coding technique are to eliminate blocking artifacts and to improve coding efficiency while maintaining a minimal complexity overhead and retaining the flexibility that block-based methods possess. Preliminary coding results confirm the validity of the proposed theory.

Wiener Filter-Based Error Resilient Time-Domain Lapped Transform

In this paper, the design of the error resilient time-domain lapped transform is formulated as a linear minimal mean-squared error problem. The optimal Wiener solution and several simplifications with different tradeoffs between complexity and performance are developed. We also prove the persymmetric structure of these Wiener filters. The existing mean reconstruction method is proven to be a special case of the proposed framework. Our method also includes as a special case the linear interpolation method used in DCT-based systems when there is no pre/postfiltering and when the quantization noise is ignored. The design criteria in our previous results are scrutinized and improved solutions are obtained. Various design examples and multiple description image coding experiments are reported to demonstrate the performance of the proposed method

Optimal block boundary pre/postfiltering for wavelet-based image and video compression

This paper presents a pre/postfiltering framework to reduce the reconstruction errors near block boundaries in wavelet-based image and video compression. Two algorithms are developed to obtain the optimal filter, based on boundary filter bank and polyphase structure, respectively. A low-complexity structure is employed to approximate the optimal solution. Performances of the proposed method in the removal of JPEG 2000 tiling artifact and the jittering artifact of three-dimensional wavelet video coding are reported. Comparisons with other methods demonstrate the advantages of our pre/postfiltering framework.

Projection-based block-matching motion estimation

This paper introduces a fast block-based motion estimation algorithm based on matching projections. The idea is simple: blocks cannot match well if their corresponding 1D projections do not match well. We can take advantage of this observation to translate the expensive 2D block matching problem to a simpler 1D matching one by quickly eliminating a majority of matching candidates. Our novel motion estimation algorithm offers computational scalability through a single parameter and global optimum can still be achieved. Moreover, an efficient implementation to compute projections and to buffer recyclable data is also presented. Experiments show that the proposed algorithm is several times faster than the exhaustive search algorithm with nearly identical prediction performance. With the proposed BME method, high-performance real-time all- software video encoding starts to become practical for reasonable video sizes.

On efficient implementation of oversampled linear phase perfect reconstruction filter banks

In this paper, we first present an alternative way of generating over-sampled linear phase perfect reconstruction filter banks (OSLP-PRFB). We show that this method provides the minimal factorization of a subset of existing OSLPPRFB. The combination of the new structure and the conventional one leads to efficient implementations of a general class of OSLPPRFB. Possible application of the new scheme is discussed.