Laplacian pyramids are used in the framework of scalable video coding to efficiently provide spatial scalability. In literature, closed-loop schemes are adopted to implement such codecs. Closed-loop introduces complexity and optimization drawbacks. In this paper we investigate mechanisms to provide an open loop version of the Laplacian pyramid. We call our approach the Laplacian pyramid with update (LPU). In this scheme, different filter-banks can be used: we propose an intuitive framework to design filters that provides efficient video coding. The interesting result of this work is that LPU enables coding performances at least equivalent to those reported for closed loop Laplacian pyramids with advantages both in terms of complexity, rate-distortion optimization and independence of the qualities of the different resolutions.

In the context of video coding, temporal wavelets represent an interesting approach because of their low complexity, encoder-decoder symmetry and multiresolution nature. However, their use at low bit-rate is limited by severe artifacts called "ghost" artifacts. In this paper we propose a controlled temporal wavelet transform which overcomes these artifacts and enables the practical use of temporal wavelets at low bit-rates. The improvements compared to standard approaches are measured in terms of PSNR, and visual quality.

The visual efficiency of an image compression technique depends directly on the amount of visually significant information it retains. By "visually significant" we mean information to which a human observer is most sensitive. The overall sensitivity depends on aspects such as contrast, color, spatial frequency, and so forth. One important aspect is the inverse relationship between contrast sensitivity and spatial frequency. This is described by the contrast sensitivity function (CSF). In compression algorithms the CSF can be exploited to regulate the quantization step-size to minimize the visibility of compression artifacts. Existing CSF implementations for wavelet-based image compression use the same quantization step-size for a large range of spatial frequencies. This is a coarse approximation of the CSF. This paper presents two new techniques that implement the CSF at significantly higher precision, adapting even to local variations of the spatial frequencies within a decomposition subband. The approaches can be used for luminance as well as color images. For color perception three different CSFs describe the sensitivity. The implementation technique is the same for each color band. Implemented into the JPEG2000 compression standard, the new techniques are compared to conventional CSF-schemes. The proposed techniques turn out to be visually more efficient than previously published methods. However, the emphasis of this paper is on how the CSF can be implemented in a precise and locally adaptive way, and not on the superior performance of these techniques.

This paper deals with the interaction and integration of two important elements of most video surveillance systems: object tracking and video coding. Both elements are open research topics and in general they are studied separately. In the domain of video surveillance the convergence of the two subjects can be extremely important to build viable applications in a number of emerging security scenarios. In this context we propose a spatio-temporal filter based on object tracking that improves coding performances and post-coding analysis. The filter is an alternative to complex object-based video codecs in many video surveillance scenarios. It increases the spatio-temporal redundancies of input signal discarding noise and unwanted moving objects. We demonstrate that the compression performances of successive codecs are increased. Moreover, the decoded stream implicitly represents the regions of interest detected on the original signal.

Human observers are very sensitive to a loss of image texture in photo-realistic images. For example a portrait image without the fine skin texture appears unnatural. Once the image is decomposed by a wavelet transformation, this texture is represented by many wavelet coefficients of low- and medium-amplitude. The conventional encoding of all these coefficients is very bitrate expensive. Instead, such an unstructured or stochastic texture can be modeled by a noise process and be characterized with very few parameters. Thus, a hybrid scheme can be designed that encodes the structural image information by a conventional wavelet codec and the stochastic texture in a model-based manner. Such a scheme, called WITCH (Wavelet-based Image/Texture Coding Hybrid), is proposed. It implements such an hybrid coding approach, while nevertheless preserving the features of progressive and lossless coding. Its low computational complexity and the parameter coding costs of only 0.01 bpp make it a valuable extension of conventional codecs. A comparison with the JPEG2000 image compression standard showed that the WITCH-scheme achieves the same subjective quality while increasing the compression ratio by more than a factor of two.

The lifting scheme (LS) is a very efficient implementation of the discrete wavelet transform (DWT). We compute the arithmetic gain realized when the LS is used instead of conventional filter banks. It is shown that, contrary to what was presented in the original work from W. Sweldens (see Appl. Comput. Harmon. Anal., vol.3, no.2, p.186-200, 1996), a gain of four is possible. However, the LS should be used with care as it can increase the memory bandwidth. Some implementations are presented together with their impact on the bandwidth. By using a common buffer for all filters, the bandwidth can be reduced to the case of the polyphase implementation. Using the method presented in this paper allows a memory bandwidth efficient implementation of the LS.

The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performance is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noise. The noise are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW algorithm. Experiment are also performed to measure the difference in terms of bit rate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression.

In image coding, the choice of a good image coding algorithm is very dependent on the image content. Based on this fact, dynamic coding algorithms have been designed. They try to find an optimal coding scheme for each image segment. They rely on an exhaustive search of the best coding algorithm. Evaluation of all algorithms is computationally very intensive and strongly limits the number of considered algorithms for a given application. Therefore, current standards rely on a single coding algorithm. This paper investigates a way to predict the coding quality from the image content. This prediction is based on a neural network. The coding quality is computed from image region features. Those features are easy and fast to compute, and are common to the whole set of considered coding algorithms. Therefore, the choice of the best algorithm can be based on those predicted coding qualities, and does not require the computation of all coding algorithms. The system is also fast enough to be used for dynamic bitrate allocation, and a simple algorithm to do this is proposed.