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Hierarchical prediction structure for subimage coding and multithreaded parallel implementation in integral imaging.

Jian Wei¹, Shigang Wang, Yan Zhao

¹College of Communication Engineering, Jilin University, Changchun 130012, China.

|April 22, 2011

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This study introduces a new coding method for 3D integral imaging data using a hierarchical prediction structure. This approach improves image quality and 3D effects while reducing coding delays for elemental image arrays.

Area of Science:

Computer Vision
Image Processing
Data Compression

Background:

Three-dimensional (3D) integral imaging faces challenges in data transmission and storage.
Elemental Image Arrays (EIAs) require efficient coding strategies for practical applications.

Purpose of the Study:

To develop an optimized coding scheme for subimage-transformed elemental images in 3D integral imaging.
To address data transmission and storage issues in 3D integral imaging systems.

Main Methods:

Preprocessing EIAs using subimage transform to obtain Subimage Arrays (SIAs).
Implementing a hierarchical prediction structure for SIA coding, inspired by the hierarchical B picture (HBP) structure in multiview video coding.
Designing a multithreaded parallel implementation considering inter-row prediction dependencies.

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Main Results:

The proposed parallel HBP scheme demonstrates superior image quality and 3D effect compared to the Hilbert-curve-based scheme.
Lower coding delay was achieved at low bit rates with the new HBP scheme.
Experimental validation was performed on both EIAs and SIAs.

Conclusions:

The hierarchical prediction structure offers an effective solution for coding 3D integral imaging data.
The multithreaded parallel implementation enhances coding efficiency and performance.
This method provides a significant improvement for 3D integral imaging data compression.