Michael T. Orchard

Novel sequential error-concealment techniques using orientation adaptive interpolation

This paper introduces a new framework for error concealment in block-based image coding systems: sequential recovery. Unlike previous approaches that simultaneously recover the pixels inside a missing block, we propose to recover them in a sequential fashion such that the previously-recovered pixels can be used in the recovery process afterwards. The principal advantage of the sequential approach is the improved capability of recovering important image features brought by the reduction in the complexity of statistical modeling, i.e., from blockwise to pixelwise. Under the framework of sequential recovery, we present an orientation adaptive interpolation scheme derived from the pixelwise statistical model. We also investigate the problem of error propagation with sequential recovery and propose a linear merge strategy to alleviate it. Extensive experimental results are used to demonstrate the improvement of the proposed sequential error-concealment technique over previous techniques in the literature.

Multiple-description video coding using motion-compensated temporal prediction

We propose multiple description (MD) video coders which use motion-compensated predictions. Our MD video coders utilize MD transform coding and three separate prediction paths at the encoder to mimic the three possible scenarios at the decoder: both descriptions received or either of the single descriptions received. We provide three different algorithms to control the mismatch between the prediction loops at the encoder and decoder. We present simulation results comparing the three approaches to two standards-based approaches to MD video coding. We show that when the main prediction loop at the encoder uses a two-channel reconstruction, it is important to have side prediction loops and transmit some redundancy information to control mismatch. We also examine the performance of our MD video coder with partial mismatch control in the presence of random packet loss, and demonstrate a significant improvement compared to more traditional approaches.

Multiple description coding for video using motion compensated prediction

We propose multiple description (MD) video coders which use motion compensated predictions. Our MD video coders utilize MD transform coding and three separate prediction paths at the encoder, to mimic the three possible scenarios at the decoder: both descriptions received or either of the single descriptions received. We provide three different algorithms to control the mismatch between the prediction loops at the encoder and decoder. The results show that when the main prediction loop is the central loop, it is important to have side prediction loops and transmit some redundancy information to control mismatch.

Performance of multiple description coders on a real channel

We explore the ability of multiple description (MD) source coders to achieve good performance on channels other than ideal MD channels. We examine both the overall system design and compare the performance of a system with MD source coder to that of a more traditional system using a layered source coder. For the memoryless channels we consider, MD source coding cannot achieve acceptable performance for a memoryless Gaussian source without appropriate channel coding. Also, in memoryless channels, a system with MD source coding outperforms a layered source coding system only in very poor channels. The introduction of memory in the channel degrades the performance of both systems equally. Using interleaving to reduce the impact of memory in the channel has more influence on performance than the choice of source coder.

Spherical Coding Algorithm for Wavelet Image Compression

In recent literature, there exist many high-performance wavelet coders that use different spatially adaptive coding techniques in order to exploit the spatial energy compaction property of the wavelet transform. Two crucial issues in adaptive methods are the level of flexibility and the coding efficiency achieved while modeling different image regions and allocating bitrate within the wavelet subbands. In this paper, we introduce the ldquospherical coder,rdquo which provides a new adaptive framework for handling these issues in a simple and effective manner. The coder uses local energy as a direct measure to differentiate between parts of the wavelet subband and to decide how to allocate the available bitrate. As local energy becomes available at finer resolutions, i.e., in smaller size windows, the coder automatically updates its decisions about how to spend the bitrate. We use a hierarchical set of variables to specify and code the local energy up to the highest resolution, i.e., the energy of individual wavelet coefficients. The overall scheme is nonredundant, meaning that the subband information is conveyed using this equivalent set of variables without the need for any side parameters. Despite its simplicity, the algorithm produces PSNR results that are competitive with the state-of-art coders in literature.

Image reconstruction from the phase or magnitude of its complex wavelet transform

This paper investigates the reconstruction of an image from the phase or magnitude of its complex wavelet transform (CWT). We view the CWT as an approximation to the analytic representation of some real wavelet coefficients and develop the conditions under which a ID signal is uniquely specified by its analytic phase or magnitude. Then, we extend the uniqueness conditions to multi- resolution and higher dimensions in order to match the situation of the CWT. In the development of the uniqueness conditions, we also gain some insights about the quality of reconstructed images and the geometrical structure of the CWT phase and magnitude representation. Our results for the CWT may also be applied to other localized phase and magnitude representations.

An adaptive edge model in the wavelet domain for wavelet image coding

State-of-art wavelet coders owe their performance to smart ideas for exploiting inter and intra-band dependencies of wavelet coefficients. We claim that developing more efficient coders requires us to look at the main source of these dependencies; i.e., highly localized information around edges. This paper investigates the structural relationships among wavelet coefficients based on an idealized view of edge behavior, and proposes a simple edge model that explains the roots of existing dependencies. We describe how the model is used to approximate and estimate the significant wavelet coefficients. Simulations support its relevance for understanding and analyzing edge information. Specifically, model-based estimation within the space-frequency quantization (SFQ) framework increases the peak signal-to-noise ratio (PSNR) by up to 0.3 dB over the original SFQ coding algorithm. Despite being simple, the model provides valuable insights into the problem of edge-based adaptive modeling of value and location information in the wavelet domain.

Phase and magnitude perceptual sensitivities in nonredundant complex wavelet representations

The recent development of a nonredundant complex wavelet transform allows a novel framework for image analysis. Work on this representation has recognized that the phase and magnitude of complex coefficients can be related to important geometric properties in images. Existing work on human visual system (HVS) sensitivity offers little guidance in understanding the relative importance of noise (e.g., introduced by lossy coding) in phase components and magnitude components. The distinct geometric significance of the two components would suggest that their respective errors relate to different types of image structure, and thus each would have its own unique HVS sensitivity. In this paper, we extend the study of just-noticeable-differences (JND) to magnitude/phase sensitivities in complex wavelet representations and outline and report on preliminary experiments characterizing them.

Image Inpainting Based on Geometrical Modeling of Complexwavelet Coefficients

The restoration of missing regions in images (inpainting) is mathematically an interpolation problem and has many important applications. This paper proposes a novel iterative inpainting algorithm based on the interpolation of the complex wavelet transform (CWT) coefficients with simple geometrical models on the magnitude and phase of the coefficients. The geometrical models describe the directionality and uniformity of the CWT magnitudes and the linearity of the CWT phases around edges and within texture areas. Both piecewise smooth signals and structured textures can be interpolated accurately with the proposed models. Motivated by the iterative reconstruction of an image from its CWT magnitude or phase, we propose an inpainting algorithm with iterative magnitude and phase estimation and CWT reconstruction. Simulation results show that the proposed algorithm achieves high PSNR and appealing visual quality with low computation complexity.

A new interpretation of translation invariant image denoising

Translation invariant (TI) image denoising outperforms orthogonal wavelet thresholding by averaging a collection of denoised estimates from different orthogonal bases. This paper proposes a new perspective of TI processing as an average of a collection of cyclic-basis frame reconstructions, each a stationary signal estimate, contrasting with the nonstationary estimates of orthogonal wavelet thresholding. This viewpoint clarifies that certain characteristics of TI (i.e. reduced edge contour artifacts) are inherited from each cyclic-basis reconstruction, rather than from the process of averaging. We relate performance advantages of TI in smooth areas of images to statistical relationships of the cyclic- basis reconstructions. In edge regions, the quality of cyclic-basis reconstructions varies significantly with pixel position relative to the edge contour. These differences couple with convexity arguments to explain large performance gains of TI in edge regions. They also suggest an improved approach to frame reconstruction, based on estimating relative location information, and identifying the best cyclic-basis reconstruction for the estimated pixel location.