Yilong Liu

Complexity comparison of fast block-matching motion estimation algorithms

Block-matching algorithms (BMA) have been widely adopted for motion estimation (ME) by real-time video coding applications due to its effectiveness and simplicity in implementation. Most fast BMAs are based on the assumption that ME matching error decreases monotonically as the search approaches the position of the global minimum error. The paper measures the contributions to computational cost reduction by different fast BMAs, including four-step search, diamond search, hexagon-based search and the recently proposed adaptive multi-mode search (AMMS). Comparison results show that the AMMS algorithm achieves a significant improvement based on the given mathematical models. Experimental coding results are also presented.

Feature-Oriented Multiple Description Wavelet-Based Image Coding

We address the problem of resilient image coding over error-prone networks where packet losses occur. Recent literature highlights the multiple description coding (MDC) as a promising approach to solve this problem. In this paper, we introduce a novel wavelet-based multiple description image coder, referred to as the feature-oriented MDC (FO-MDC). The proposed multiple description (MD) coder exploits the statistics of the wavelet coefficients and identifies the subsets of samples that are sensitive to packet loss. A joint optimization between tree-pruning and quantizer selection in the rate-distortion sense is used in order to allocate more bits to these sensitive coefficients. When compared with the state-of-the-art MD scalar quantization coder, the proposed FO-MDC yields a more efficient central-side distortion tradeoff control mechanism. Furthermore, it proves to be more robust for image transmission even with high packet loss ratios, which makes it suitable for protecting multimedia streams over packet-erasure channels

Low Bit-Rate Image Coding Based on Pyramidal Directional Filter Banks

This paper presents a novel embedded image coding system based on the pyramidal multidirectional image representation. The multiscale filter bank is a combination of the overcomplete pyramidal directional filter bank at higher scales and the traditional maximally-decimated wavelet filter bank at lower scales, to provide a sparse image representation. The coding algorithm then efficiently clusters the significant coefficients using progressive morphological operations. Context models for arithmetic coding are designed to exploit the intra-band dependency and the correlation existing among the neighboring directional subbands. Experimental results show that the proposed coding algorithm outperforms the current state-of-the-art wavelet based coders, such as JPEG2000, for images with directional features

Fractional-pel motion refinement based on hierarchical adjustable dual-parabola model

H.264 is the new video standardization recommended by the ITU-T Joint Video Coding Experts Group (JVT), in which fractional pixel motion compensation is applied in order to achieve more accurate motion description and higher compression efficiency. However, it remarkably increased complexity in the motion estimation process. Therefore, reducing the computation load incurred by fractional-pel search is necessary. An efficient fast fractional-pel motion refinement algorithm is proposed, based on a hierarchical adjustable dual-parabola (HADP) model. The significant fractional-pel positions can be accurately selected based on the adaptive HADP model, which greatly eliminates unnecessary fractional-pel positions.

Implementational aspects of the contourlet filter bank and application in image coding

This paper analyzed the implementational aspects of the contourlet filter bank (or the pyramidal directional filter bank (PDFB)), and considered its application in image coding. First, details of the binary tree-structured directional filter bank (DFB) are presented, including a modification to minimize the phase delay factor and necessary steps for handling rectangular images. The PDFB is viewed as an overcomplete filter bank, and the directional filters are expressed in terms of polyphase components of the pyramidal filter bank and the conventional DFB. The aliasing effect of the conventional DFB and the Laplacian pyramid to the directional filters is then considered, and the conditions for reducing this effect are presented. The new filters obtained by redesigning the PDFBs satisfying these requirements have much better frequency responses. A hybrid multiscale filter bank consisting of the PDFB at higher scales and the traditional maximally decimated wavelet filter bank at lower scales is constructed to provide a sparse image representation. A novel embedded image coding system based on the image decomposition and a morphological dilation algorithm is then presented. The coding algorithm efficiently clusters the significant coefficients using progressive morphological operations. Context models for arithmetic coding are designed to exploit the intraband dependency and the correlation existing among the neighboring directional subbands. Experimental results show that the proposed coding algorithm outperforms the current state-of-the-art wavelet-based coders, such as JPEG2000, for images with directional features.

Embedded image coding using quincunx directional filter bank

An image coding system based on combination of a multiresolution directional decomposition and a morphological dilation algorithm is proposed in this paper. The multiscale filter bank combines the recently introduced non-uniform quincunx directional filter bank at higher scales and the traditional wavelet filter bank at lower scales, to provide a sparse image representation. The coding algorithm then efficiently clusters the significant coefficients using progressive morphological dilation. The remaining scattered significant coefficients are coded with the aid of the Tarp filter. The proposed image coding algorithm achieves better results than the current state-of-the-art wavelet based coders, such as SPIHT and JPEG2000

Drift-free multiple description video coding with redundancy rate-distortion optimization

Multiple description coding (MDC) has been shown to be robust for video transmission over error-prone channels. By applying the extra prediction loops to the multiple description transform video coder, the mismatching error (drift) incurred by the one-channel pairwise correlating transform reconstruction is decreased. However, the problem of effective redundancy allocation is introduced by the drift coding. In this paper, we propose an approach to optimize the drift-free multiple description video coding scheme. The optimization is in the redundancy rate-distortion sense using the Lagrangian relaxation method for optimum allocation of redundancy amongst the correlating transform and the drift coding. The experimental results show that our algorithm achieves substantial gains over the conventional MDC coder.

Feature-oriented multiple description image coding

In this paper, we introduce a novel wavelet-based multiple description image coder, referred to as the feature-oriented MDC (FO-MDC). The proposed multiple description coders exploit the statistics of the wavelet coefficients, and identify the subsets of samples those are sensitive to packet loss. A joint optimization between tree-pruning and quantizer selection in the rate-distortion sense is used in order to allocate more bits to these sensitive coefficients. When compared with the state-of-the-art multiple description scalar quantization coders, the proposed FO-MDC yields a more efficient central-side distortion tradeoff control mechanism. Furthermore, it proves to be more robust for image transmission even with high packet loss ratios