Heechan Park

Fast Inter-Mode Selection in the H.264/AVC Standard Using a Hierarchical Decision Process

A complexity reduction algorithm tailored for the H.264/AVC encoder is described. It aims to alleviate the computational burden imposed by Lagrangian rate distortion optimization in the inter-mode selection process. The proposed algorithm is described as a hierarchical structure comprising three levels. Each level targets different types of macroblocks according to the complexity of the search process. Early termination of mode selection is triggered at any of the levels to avoid a full cycle of Lagrangian examination. The algorithm is evaluated using a wide range of test sequences of different classes. The results demonstrate a reduction in encoding time of at least 40%, regardless of the class of sequence. Despite the reduction in computational complexity, picture quality is maintained at all bit rates.

Progressive mesh-based motion estimation using partial refinement

A technique for performing progressive mesh-based motion estimation in a layered fashion is presented. Motion compensation based on image warping provides a block prediction free of block artifacts. The smooth prediction can be used to identify motion-active regions by comparing with the reference frame and generate a partial denser mesh, thus forming layers of mesh. This approach provides a hierarchical partial refinement according to motion activity without additional cost. Experimental results indicate that the technique shows improvement over a single-layered uniform mesh and advantages over block-based techniques, particularly in scalable and very low bitrate video coding.

Compact representation of contours using directional grid chain code

An efficient contour-based method for the coding of binary shape information is described. Conventional chain coding techniques show high coding efficiency for lossless compression, but they exploit the coherence of the contour in only a restricted manner. Higher coding efficiency can be achieved by realising the neighbourhood relation as a Markov chain, and this is exploited in a new coding scheme, the directional grid chain coding (DGCC). The method is computationally efficient and the coding process adapts to the inherent changes in the contour. Two schemes are proposed, a lossless and a quasi-lossless method. The lossless scheme achieves 32% saving in bit rate compared with the conventional differential chain code (DCC). The second, quasi-lossless technique achieves 44% bit reduction compared with the DCC and the distortions present in the reconstructed contour are hardly noticeable to the human eye.

Structural Texture Segmentation using Affine Symmetry

Many natural textures comprise structural patterns and show strong self-similarity. We use affine symmetry to segment an image into self-similar regions; that is a patch of texture (blocks from a uniformly partitioned image) can be transformed to other similar patches by warping. If the texture image contains multiple regions, we then cluster patches into a number of classes such that the overall warping error is minimized. Discovering the optimal clusters is not trivial and known methods are computationally intensive due to the affine transformation. We demonstrate efficient segmentation of structural textures without affine computation. The algorithm uses Fourier Slice Analysis to obtain a spectral contour signature. Experimental evaluation on structural textures shows encouraging results and application on natural images demonstrates identification of texture objects.

An affine symmetric approach to natural image compression

We approach image compression using an affine symmetric image representation that exploits rotation and scaling as well as the translational redundancy present between image blocks. It resembles fractal theory in the sense that a single prototypical block is needed to represent other similar blocks. Finding the optimal prototypes is not a trivial task particularly for a natural image. We propose an efficient technique utilizing independent component analysis that results in near-optimal prototypical blocks. A reliable affine model estimation method based on Gaussian mixture models and modified expectation maximization is presented. For completeness, a parameter entropy coding strategy is suggested that achieves as low as 0.14 bpp. This study provides an interesting approach to image compression although the reconstruction quality is slightly below that of some other methods. However the high frequency details are well-preserved at low bitrates, making the technique potentially useful in low bandwidth mobile applications.

An Affine Symmetric Image Model and Its Applications

Natural images contain considerable redundancy, some of which is successfully captured using recently developed directional wavelets. In this paper, an affine symmetric image model is considered. It provides a flexible scheme to exploit geometric redundancy. A patch of texture from an image is rotated, scaled and sheared to approximate other similar parts in the image, revealing the self-similarity relation. The general scheme is derived as follows. A texture model is required that identifies structural patterns. Then the affine symmetry is exploited between structural textures at a local level, the objective being to find the minimum residual error by estimating the affine transform relating two patches of texture. Having developed a local model, the methodology is extended to the whole image to estimate the global affine relation. This global model is further developed in a multiresolution framework for multiscale analysis, by which the self similarity of the image is exploited across space and scale. The multiresolution model can be applied to a series of practical problems. Experimental evaluation demonstrates the effectiveness of the approach in affine invariant texture segmentation and image approximation.

Local Affine Image Matching and Synthesis Based on Structural Patterns

A general purpose block-to-block affine transformation estimator is described. The estimator is based on Fourier slice analysis and Fourier spectral alignment. It shows encouraging performance in terms of both speed and accuracy compared to existing methods. The key elements of its success are attributed to the ability to: 1) locate an arbitrary number of affine invariant points in the spectrum that latch onto significant structural features; 2) match the estimated invariant points with the target spectrum by the slicewise phase-correlation; and 3) use affine invariant points to directly compute all linear parameters of the full affine transform by spectral alignment. Experimental results using a wide range of textures are presented. Potential applications include affine invariant image segmentation, registration, affine symmetric image coding, and motion analysis.

Image Denoising with Directional Bases

Directional information is an important component of both natural and synthetic images, and it is exploited in many image processing applications. Directional basis analysis is used to capture significant structural information. This paper presents an empirical study of image denoising with directional bases. We consider two distinct approaches. One involves the multi-resolution Fourier transform (MFT) facilitated with a multi-directional selective filter. The other is based on statistics, independent component analysis (ICA) that adaptively decomposes an image into a set of directional bases. We then present a combined approach that benefits from the computational efficiency of the MFT and the data adaptiveness of ICA. Experimental results are compared with those from other recent directional transforms such as the curvelet and directional cosine transform.

Improved schemes for inter-frame coding in the H.264/AVC standard

An efficient algorithm for inter-frame coding in the H.264/AVC standard is extended to provide more significant speedup in computational performance for sequences containing high spatial correlation and motion. The proposed scheme features a more sophisticated search process and robust predictions to achieve better PSNR-rate performance for a large range of compression levels. Extensive simulation results demonstrate speedups of between 41% and 68%, with no noticeable deterioration in picture quality or compression ratio, even for the coding of complex video sequences.

Fast mesh-based motion estimation employing an embedded block model

A fast algorithm for mesh-based motion estimation employing uniform triangular patches is proposed. The technique utilises an embedded block model to estimate the motion of the mesh grid points. Without the need for time-consuming evaluation, the algorithm reduces the number of search iterations according to the inherent motion. A block-wise coding approach is taken for the motion information, permitting any picture degradation caused by the fast algorithm to be successfully compensated by the residue coding. Simulations on three classes of test sequence show that the proposed algorithm results in a better PSNR-rate performance than the hexagonal matching algorithm. Moreover, a reduction of up to 91% in mesh iterations is obtained