Albert A. Deknuydt

Three-dimensional scene reconstruction from images

Modeling of 3D objects from image sequences is a challenging problem and has been a research topic for many years. Important theoretical and algorithmic results were achieved that allow to extract even complex 3D models of scenes from sequences of images. One recent effort has been to reduce the amount of calibration and to avoid restrictions on the camera motion. In this contribution an approach is described which achieves this gaol by coming state-of-the-art algorithms for uncalibrated projective reconstruction, self- calibration and dense correspondence matching.

Color space choice for nearly reversible image compression

In applications where nearly reversible color image compression is required, the choice of an appropriate color space is a major factor determining the attainable compression ratio. The optimal choice would satisfy the following conditions: (1) Decorrelate the data as much as possible. (2) Minimize the total number of bits in the data path needed for a certain quality level (assuming a truly reversible component compression scheme). (3) Need no calculations with critical accuracy. We compared several known linear color transformations (under the assumption that the original data was represented in RGB) with respect to these conditions. Decorrelation and entropy reduction were calculated. The number of bits required to guarantee a certain quality level measured in (Delta) Lab units was determined. The effects of transformation coefficient quantization were checked. As a result, a simple transformation needing no multipliers at all is proposed.

Space domain coding method, suitable for coding motion-compensated difference images

For a long time, most codes for moving images have been of the hybrid DCT type. However, the use of DCT as a method to code notion compensated difference images is largely based on tradition rather than proof. DCT proved to be nearly optimal for a certain kind of statistics that matches the real statistics of images rather well. But motion compensated difference images do not fail under this category, as their local spatial correlation is much lower. Therefore, there is no solid reason to use DCT for coding them, other than the fact that DCT is well known and that hardware for it is readily available. In this paper, we propose a straightforward spatial domain way of coding images, here called optimal level allocation. It proves to be suitable for both intra and inter images.

Motion adaptive quantization in transform coding for exploiting motion masking effect

In this paper, we investigate transform quantization scheme for exploiting motion masking effect (MME). The study is carried out by adapting the quantizer with respect to the movement of the image. The idea of the strategy is based on a velocity dependent HVS model. Through simulation, motion adaptive quantization (MAQ) is proved effective with intra field coding. A scheme of a motion compensated DCT coding with MAQ is then presented. Technical points essential to its realization are explained.

Using subjective redundancy for DCT coding of moving images

An investigation is carried out on employing subjective redundancy to improve DCT coding respecting the non-trackable motion in video signals. The human visual system (HVS) is exploited through the two aspects of its behavior, the sensitivity to a visual stimulus and visual masking. Some measures possible to adopt those aspects are discussed and tested. The result showed that: (1) during the non-trackable motion, a reduction of the HVSs response to the reconstruction error occurs because of the loss of sensitivity and the increase of the masking effect; (2) as far as the sensitivity is concerned, adjusting the quantization weighting function with respect to the motion is unlikely to bring substantial improvement; (3) the magnitude of the visual masking by the image content over the reconstruction error is positively related to the function of spatial masking and the relative speed between the eyes and the image. An example of application on motion compensated DCT coding is presented.

Simple algorithmm to extract realistic motion fields out of video sequences

In video coding it is clearly worthwhile to have a more realistic motion field than what can be obtained by the classical mean absolute difference (MAD) full search method. Applications can be found in very low bitrate coding where the amount of bits to code the motion field is important compared to the total bitrate, in codecs which want to exploit some features of the human visual system (classification for optimal bit allocation, use of motion masking...) or in object based coding where the motion estimation algorithm interacts with the segmentation procedure. Our research started from the well known MAD full search procedure and wanted to obtain reasonable results without adding too much complexity. The improvement is performed inside the algorithm without any need for post processing. After a more thorough description of these improvements, some results will be compared and applications indicated.

Simple illumination-corrected vector search algorithm

Plain block matching, and may improved block based matching techniques, fail to determine real physical motion when there is a marked change in luminance. Such changes occur often, but most of the time, the block average changes only a few units per frame, if the frame rate is reasonably high. This is not enough to completely upset plain block matching. When special effects are present, or frame rates are low, the effect becomes more important. Then vectors returned by plain block matching try to match the change in average luminance. What happens as a result of this depends on the exact codec used. Some coders will find the resulting total difference energy too large, and switch to intra mode coding. An example of this behavior is the problem most MPEG encoders have with photographic flashlights. A more intelligent codec might still code in inter mode, but will have to recode all details in the block, because of the wrong vector. If we could use a vector that keeps track of real physical motion and a codec that can deal with DC changes separately, we would be much better off. In this paper we describe a simple way to correct for these illumination changes, without adding any worthwhile computational burden to the encoder and show that the additional to an improved block matching algorithm is able to generate more realistic motion vectors.

Segmentation-based video codec with a block-based fallback mode

This paper describes a possible strategy to migrate from block-based coding towards object-based coding. We use the results of an intelligent motion estimation algorithm to define low resolution (block based) segmentation. The information from previous segmentation results together with the motion vector field, the image data and spatial relations are used to define the cost to belong to a given object. A separate selection procedure is used to generate the high resolution (pixel based) objects. Afterwards some shape simplification is performed to generate the final objects.

Description and evaluation of a non-DCT-based codec

At this time, almost all (de-facto) video coding standards are DCT based. It would be wrong though to think that DCT is the only practical way to reach a reasonable compression ratio for a reasonable codec complexity. In this paper, a description of a complete hybrid codec based on OLA (Optimal Level Allocation) and HVS (Human Visual System) based classification is given. Then a performance comparison is made between this codec and an MPEG-2 like codec.

Automatic segmentation for very low bit-rate video coding

Object based coding is a new technique which is being investigated to achieve high compression ratios for video sequences. The classical approach to video coding is a four- step algorithm: segmentation, motion estimation and compensation, coding of the segmentation information and finally coding of the prediction errors. In order to reduce the number of bits needed for coding the objects, we propose another approach where we try to combine the first three steps: do the motion estimation and segmentation together and at the same time collect important information for the coding of the segmentation information.