Cedric Nishan Canagarajah

Image fusion using complex wavelets

The fusion of images is the process of combining two or more images into a single image retaining important features from each. Fusion is an important technique within many disparate fields such as remote sensing, robotics and medical applications. Wavelet based fusion techniques have been reasonably effective in combining perceptually important image features. Shift invariance of the wavelet transform is important in ensuring robust subband fusion. Therefore, the novel application of the shift invariant and directionally selective Dual Tree Complex Wavelet Transform (DT-CWT) to image fusion is now introduced. This novel technique provides improved qualitative and quantitative results compared to previous wavelet fusion methods.

Enhanced error concealment with mode selection

Delay sensitive video transmission over error prone networks can suffer from packet erasures when channel conditions are not favorable. Use of error concealment (EC) at the video decoder is necessary in such cases to prevent error induced artefacts making the affected video frames visibly intolerable. This paper proposes an EC method that incorporates enhanced temporal and spatial concealment elements, the use of which is controlled by a mode selection (MS) algorithm well matched to the characteristics of the temporal concealment approach. The performance of the individual enhancements and of the MS algorithm are compared with the respective features of the method employed in the H.264 joint model (JM) decoder and with other state of the art methods. The overall performance of the proposed method is shown to offer significant gains (up to 9 dB) compared to that of the JM decoder for a wide range of natural and animation image sequences without any considerable increase in complexity

Fade and dissolve detection in uncompressed and compressed video sequences

Automatic identification of special effects is a prerequisite for video indexing and intelligent video encoding. In this paper we present an algorithm for fade and dissolve scene change detection in video sequences. We use statistical features of the images to identify these special effects in uncompressed video. DC-estimation is used to evaluate statistical features both in H.263 and MPEG-2 compressed video. Results show that these special effects can be identified accurately with the proposed scheme.

Interpolation Free Subpixel Accuracy Motion Estimation

Subpixel motion estimation plays an important role in compression efficiency within modern video codecs such as MPEG2, MPEG4, and H.264. Subpixel motion estimation is implemented within these standards using interpolated values at 1/2 or 1/4 pixel accuracy. Such interpolation gives a good reduction in residual energy for each predicted macroblock and, therefore, improves compression. However, this leads to a significant increase in computational complexity at the encoder. This is especially true for H.264 where the cost of an exhaustive set of macroblock segmentations need to be estimated in order to obtain an optimal mode for prediction. This paper presents a novel interpolation-free scheme for subpixel motion estimation using the result of the full pixel sum of absolute difference distribution of each motion compensated block applied to an H.264 encoder. This system produces reduced complexity motion estimation with a controllable tradeoff between compression performance and encoder speed. These methods facilitate the generation of a real time software H.264 encoder

A unified approach to scene change detection in uncompressed and compressed video

There is an urgent need to extract key information automatically from video for the purposes of indexing, fast retrieval and scene analysis. To support this vision, reliable scene change detection algorithms must be developed. This paper describes a novel unified algorithm for scene change detection in uncompressed and MPEG-2 compressed video sequences using statistical features of images. Results on video of various content types are reported and validated with the proposed scheme in uncompressed and MPEG-2 compressed video. Furthermore, results show that the accuracy of the detected transitions is above 95% and 90% for uncompressed and MPEG-2 compressed video respectively.

Rotationally invariant texture features using the dual-tree complex wavelet transform

New rotationally invariant texture feature extraction methods are introduced that utilise the dual-tree complex wavelet transform (DT-CWT). The complex wavelet transform is a new technique that uses a dual tree of wavelet filters to obtain the real and imaginary parts of complex wavelet coefficients. When applied in two dimensions the DT-CWT produces shift invariant orientated subbands. Both isotropic and anisotropic rotationally invariant features can be extracted from the energies of these subbands. Using simple minimum distance classifiers, the classification performance of the proposed feature extraction methods were tested with rotated sample textures. The anisotropic features gave the best classification results for the rotated texture tests, outperforming a similar method using a real wavelet decomposition.

Simplex minimization for single- and multiple-reference motion estimation

Block-matching motion estimation (BMME) can be formulated as a 2-D constrained minimization problem. This problem can, therefore, be solved with reduced complexity using optimization techniques. This paper proposes a novel fast BMME algorithm called the simplex minimization search (SMS). The algorithm is based on the simplex minimization (SM) optimization method. The initialization procedure, termination criterion, and constraints on the independent variables of the search are designed to take advantage of the characteristics of the BMME problem and the properties of the block motion fields of typical video sequences. Simulation results show that the proposed algorithm outperforms other fast BMME algorithms, providing better prediction quality, a smoother motion field, and higher speed-up ratio. This paper also investigates the properties of the multiple-reference (MR) block motion field. Guided by the results of this investigation, the paper extends the SMS algorithm to the MR case. Three MR SMS algorithms are proposed, providing different degrees of compromise between prediction quality and computational complexity. Simulation results using 50 reference frames indicate that the proposed MR algorithms have a computational complexity comparable to that of single-reference full-search while still maintaining the prediction gain of MR motion estimation.

Digital watermarking in wavelet domain with predistortion for authenticity verification and localization

In this paper, we present a blind fragile authentication algorithm by modifying a robust algorithm. The embedding process modifies the relative position of one wavelet coefficient from a vector of 3 coefficients. The introduced distortion of the watermarking system is reduced by a content dependent quantization parameter. This parameter refines the quantization step according to the magnitude of the coefficients in the vector. The smallest wavelet coefficients in the smooth areas of the image are pre-distorted to improve the performance and efficiency of the algorithm in these areas. This pre-distortion does not visually degrade the image as the introduced high frequency noise is evenly distributed over these areas. A dichotomous detector compares the extracted and embedded watermark on a bit by bit basis. This results in a high detection resolution, which can deliver information about the shape of the modified object. Embedding of the watermark with a larger redundancy increases the robustness of the system to additive white Gaussian noise attack. A weighted estimation then extracts the embedded watermark. This technique is fully described in the paper. Experimental results of this system embedded in the wavelet domain illustrate the performance and effectiveness compared with other reported fragile watermarking methods.

Multiple-reference temporal error concealment

This paper demonstrates the benefits of extending the temporal error concealment process to more than one reference frame. In this context, the paper proposes two multiple-reference temporal concealment techniques. Both techniques are extensions of the single-reference motion field interpolation (MFI) technique. The first technique uses MFI to provide candidate concealments from a number of reference frames. Boundary matching (BM) is then used to select one concealment from this set of candidates. In the second technique, multi-hypothesis compensation is used to combine the candidate concealments. In other words, the concealment in this case is a weighted sum of all candidate concealments. The superior performance of the proposed techniques is demonstrated within a multiple-reference video codec over a range of error rates and frame distances.

Complex wavelet domain image fusion based on fractional lower order moments

We describe an image fusion algorithm for data exhibiting heavy tails with no convergent second- or higher-order moments. Our developments rely on recent results showing that wavelet decomposition coefficients of images are best modeled by alpha-stable distributions, a family of heavy-tailed densities. Thus, in the multiscale wavelet domain we develop a novel fusion rule based on fractional lower order moments (FLOMs). Simulation results show that our method achieves better performance in comparison with previously proposed pixel-level fusion approaches.