Sequential block-based disparity map estimation algorithm for stereoscopic image coding
- Resource Type
- Authors
- Gabriel Dauphin; Aysha Kadaikar; Anissa Mokraoui
- Source
- Signal Processing: Image Communication. 39:159-172
- Subject
- Image coding
Reference view
Binary number
Stereoscopy
law.invention
Tree structure
law
Signal Processing
Entropy (information theory)
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Raster scan
Algorithm
Software
Mathematics
Block-matching algorithm
- Language
- ISSN
- 0923-5965
This paper deals with the problem of block-based disparity map estimation for stereoscopic image coding where the estimated map is transmitted to the decoder in order to predict one view from the other. The estimation problem of the disparity map is thus a trade-off between the quality prediction and the binary cost of the disparities to be stored or transmitted. This trade-off is modeled as a joint entropy-distortion metric assuming that the disparity map is encoded with an entropy coder; and one of the two views is fully predicted using this map when applied to the other view. However minimizing this joint metric is a complex combinatorial optimization problem where choices of disparities are all interrelated. A sub-optimal optimization solution is then proposed. It is based on a tree structure which is constructed sequentially whenever a block is matched. The developed algorithm, called Modified M-Algorithm (MMA), processes the reference view in a raster scanning order and assumes that the disparities to be selected in the unprocessed area are likely to follow a chosen disparity distribution. This algorithm has the ability at each step of the process not only to retain the M-best paths of the tree in terms of entropy-distortion cost but also to explore all possible extensions of each of these M paths until reading the last block of the view. Simulations, conducted on stereoscopic images extracted from Middlebury and Deimos datasets, show the advantage of our MMA compared to the conventional Block Matching Algorithm (BMA) with and without regularization both in terms of reducing bitrate and distortion. HighlightsA block-based disparity map estimation algorithm for stereo image coding is proposed.The optimization algorithm relies on a joint entropy-distortion metric.A sub-optimal disparity map is built sequentially using a simplified tree structure.Comparisons are done with block-matching algorithms with and without regularization.Simulation results show significant gains in terms of rate-distortion.