We propose a new framework for highly scalable video compression, using a lifting-based invertible motion adaptive transform (LIMAT). We use motion-compensated lifting steps to implement the temporal wavelet transform, which preserves invertibility, regardless of the motion model. By contrast, the invertibility requirement has restricted previous approaches to either block-based or global motion compensation. We show that the proposed framework effectively applies the temporal wavelet transform along a set of motion trajectories. An implementation demonstrates high coding gain from a finely embedded, scalable compressed bit-stream. Results also demonstrate the effectiveness of temporal wavelet kernels other than the simple Haar, and the benefits of complex motion modeling, using a deformable triangular mesh. These advances are either incompatible or difficult to achieve with previously proposed strategies for scalable video compression. Video sequences reconstructed at reduced frame-rates, from subsets of the compressed bit-stream, demonstrate the visually pleasing properties expected from low-pass filtering along the motion trajectories. The paper also describes a compact representation for the motion parameters, having motion overhead comparable to that of motion-compensated predictive coders. Our experimental results compare favorably to others reported in the literature, however, our principal objective is to motivate a new framework for highly scalable video compression.

This paper proposes a new framework for the construction of motion compensated wavelet transforms, with application to efficient highly scalable video compression. Motion compensated transform techniques, as distinct from motion compensated predictive coding, represent a key tool in the development of highly scalable video compression algorithms. The proposed framework overcomes a variety of limitations exhibited by existing approaches. This new method overcomes the failure of frame warping techniques to preserve perfect reconstruction when tracking complex scene motion. It also overcomes some of the limitations of block displacement methods. Specifically, the lifting framework allows the transform to exploit inter-frame redundancy without any dependence on the model selected for estimating and representing motion. A preliminary implementation of the proposed approach was tested in the context of a scalable video compression system, yielding PSNR performance competitive with other results reported in the literature.

A scalable video coder cannot be equally efficient over a wide range of bit rates unless both the video data and the motion information are scalable. We propose a wavelet-based, highly scalable video compression scheme with rate-scalable motion coding. The proposed method involves the construction of quality layers for the coded sample data and a separate set of quality layers for the coded motion parameters. When the motion layers are truncated, the decoder receives a quantized version of the motion parameters used to code the sample data. The effect of motion parameter quantization on the reconstructed video distortion is described by a linear model. The optimal tradeoff between the motion and subband bit rates is determined after compression. We propose two methods to determine the optimal tradeoff, one of which explicitly utilizes the linear model. This method performs comparably to a brute force search method, reinforcing the validity of the linear model itself. Experimental results indicate that the cost of scalability is small. In addition, considerable performance improvements are observed at low bit rates, relative to lossless coding of the motion information.

An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

This paper continues the development of a new framework for the construction of motion compensated wavelet transforms for highly scalable video compression. The current authors recently proposed a motion adaptive wavelet transform based on motioncompensated lifting steps. This approach overcomes several limitations of existing methods. In particular, frame warping and block displacement methods cannot efficiently exploit complex motion without sacrificing invertibility. By contrast, the motion-compensated lifting transform remains invertible regardless of the motion model. The previous work was primarily in the context of a block motion model. However, block motion models inevitably yield discontinuous motion fields, which poorly represent complex motion in real video sequences. In this paper we consider the benefits of a continuous motion field, by incorporating a deformable mesh motion model into the existing framework. Experimental results show that this leads to improved compression and visual quality. In addition, we show that the invertibility of continuous motion fields allows greater potential for compactly representing the motion information.

Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

The G-protein coupled receptor (GPCR) superfamily fulfils various metabolic functions and interacts with a diverse range of ligands. There is a lack of sequence similarity between the six classes that comprise the GPCR superfamily. Moreover, most novel GPCRs found have low sequence similarity to other family members which makes it difficult to infer properties from related receptors. Many different approaches have been taken towards developing efficient and accurate methods for GPCR classification, ranging from motif-based systems to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of their amino acid sequences. This review describes the inherent difficulties in developing a GPCR classification algorithm and includes techniques previously employed in this area.

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