Browsing by Author "Chung-Neng"

Now showing 1 - 4 of 4

Architecture and method for fine granularity scalable video coding
(2007-04-10)
Huang
;
Hsiang-Chun
;
Wang
;
Chung-Neng
;
Chiang
A robust fine granularity scalability video encoding includes a base layer encoder and an enhancement layer encoder in which motion compensated difference images are generated by comparing an original image to predicted images at base layer and enhancement layer with motion compensation. Based on leaky and partial predictions, a high quality reference image is constructed at the enhancement layer to improve temporal prediction. In the construction of the high quality reference image, one parameter β controls the number of bitplanes of the enhancement layer difference coefficients used and another parameter α controls the amount of predictive leak. A spatial scalability module allows the processed pictures at the base layer and the enhancement layer to have identical or different spatial resolutions.
Architecture and method for fine granularity scalable video coding
(2006-07-04)
Huang
;
Hsiang-Chun
;
Wang
;
Chung-Neng
;
Chiang
A robust fine granularity scalability video encoding includes a base layer encoder and an enhancement layer encoder in which motion compensated difference images are generated by comparing an original image to predicted images at base layer and enhancement layer with motion compensation. Based on leaky and partial predictions, a high quality reference image is constructed at the enhancement layer to improve temporal prediction. In the construction of the high quality reference image, one parameter β controls the number of bitplanes of the enhancement layer difference coefficients used and another parameter α controls the amount of predictive leak. A spatial scalability module allows the processed pictures at the base layer and the enhancement layer to have identical or different spatial resolutions.
Fast motion estimation using N-queen pixel decimation
(2006-04-11)
Wang
;
Chung-Neng
;
Yang
;
Shin-Wei
;
Liu
The present invention proposes a fast motion estimation using N-queen pixel decimation, whereby after a reference block and a block to be processed are selected in a video sequence, an N×N queens pattern is used for pixel decimation to perform block match, thereby obtaining a good enough block difference value. The present invention combines pixel decimation with fast motion estimation for search points reduction to achieve the object of simplifying computational complexity of motion estimation. Therefore, the present invention can sieve out sufficiently representative pixels and will not increase extra computational complexity.
Method and apparatus for motion estimation with all binary representation
(2006-03-28)
Luo
;
Jeng-Hung
;
Lee
;
Gen-Ming
;
Wang
A method of motion estimation for video encoding constructs a binary pyramid structure having three binary layers. A state update module registers and updates repeat occurrence of final motion vectors and a static-state checking module determines if the method is in a static mode or a normal mode based on the repeat occurrence. In a normal mode, the first binary layer is searched within a ±3 pixel refinement window to determine a first level motion vector. In the second binary layer, a search range is computed based on six motion vector candidates. By checking every point within in the search range, a second binary layer search generates a second level motion vector. Finally, a third binary layer search within a ±2 pixel refinement window generates a final motion vector according to the second level motion vector. In a static mode, a fine tuning module performs search within a ±1 pixel refinement window and generates a final motion vector.