A monotonicity principle for information theory

The applicability of MPEG video coding can be improved by scaling the resource usage, considering the desired application and the device that is going to be used. For this purpose, we present a new resource-scalable motion estimation technique, based on a flexible three-stage process. This process involves frame processing in display order independent of GOP structures and approximation of MPEG motion-vector fields using multiple references. Quality refinements are optional, giving a flexible framework with a large scalability range (with a factor of 14) of computational effort and memory bandwidth usage, resulting in different picture-quality levels or bitrates. Experiments show that in high-quality operation, the new method slightly outperforms a full-search motion estimation with a search window of 32 x 32 pixels, although it is based on a recursive block matcher that has only a fraction of the complexity of a full-search block matcher. For e.g. mobile applications with power constraints, the computational effort can be further reduced by at least 43% compared to a single-pass recursive block matcher, at an expense of a slight increase of the bitrate (0.013 bits/pixel)

Computational-complexity scalable motion estimation for mobile MPEG encoding

Complexity scalability algorithms are important for mobile consumer devices using MPEG video coding, because they offer a trade-off between picture quality and the embedded available computational performance. This paper presents a new scalable three-stage motion estimation technique, which includes preprocessing of frames in display order and approximation of motion-vector fields using multiple temporal references. A quality refinement of the approximation is added as an optional stage. Furthermore, we present a new scalable motion-estimation algorithm, based on simple edge detection, for integration into the above-mentioned new three-stage motion estimation technique. The complete system provides a flexible framework with a large scalability range in computational effort, resulting in an output quality that scales up smoothly with the number of operations spent on the motion estimation process. Experiments show a scalable computational effort from below one SAD (sum of absolute difference) computation per macroblock up to 15 SAD computations, resulting in a global variation of 7.4 dB PSNR in picture quality (with the Stefan sequence). In high-quality operation, the new algorithm is comparable to (or even outperforms) a full-search motion estimation with a search window of 32×32 pixels

Unit for and method of estimating a current motion vector

A motion estimation unit (500) for estimating a current motion vector comprises a match error unit (506) for calculating match errors of respective candidate motion vectors and a selector (508) for selecting the current motion vector from the candidate motion vectors by means of comparing the match errors of the respective candidate motion vectors. Some of the candidate motion vectors are extracted from a set of previously estimated motion vectors. Other candidate motion vectors are calculated based on multiple motion vectors which are selected from the set of previously estimated motion vectors

Unit for and method of estimating a current motion vector

A motion estimation unit (500) for estimating a current motion vector comprises a match error unit (506) for calculating match errors of respective candidate motion vectors and a selector (508) for selecting the current motion vector from the candidate motion vectors by means of comparing the match errors of the respective candidate motion vectors. Some of the candidate motion vectors are extracted from a set of previously estimated motion vectors. Other candidate motion vectors are calculated based on multiple motion vectors which are selected from the set of previously estimated motion vectors

Method and system to encode a set of input values into a set of coefficients using a given algorithm

The invention provides coding (20) a set of input values (S1) into a set of coefficients by use of a given algorithm, by selecting (201) coefficients to be calculated, out of a total set of possible coefficients that can be calculated by the given algorithm given the set of input values, in which selecting higher priority is given to coefficients which require a lower calculation cost compared to other coefficients, and by calculating (201) the selected coefficients to obtain the set of coefficients.; Preferably, for a given coefficient the calculation cost is at least partly based on an amount of calculation steps that is required to calculate the given coefficient reduced with an amount of calculations that can be shared with the calculation of other selected coefficients, and wherein in the step of calculating results of shared calculation steps are re-used in calculating (201) other coefficients which share the shared calculation steps

Undescribed species of Nearctic Tipulidae (Diptera). IV

Volume: 24Start Page: 19End Page: 2