Three-level parallel-set partitioning in hierarchical trees coding based on the collaborative CPU and GPU for remote sensing images compression

Hao Chen; Anqi Wei; Ye Zhang

doi:10.1117/1.JRS.11.045015

18 December 2017 Three-level parallel-set partitioning in hierarchical trees coding based on the collaborative CPU and GPU for remote sensing images compression

Hao Chen, Anqi Wei, Ye Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 045015 (December 2017). https://doi.org/10.1117/1.JRS.11.045015

Abstract

To accelerate the massive remote sensing images (RSIs) coding in a ground service-oriented remote sensing system, this study proposes three-level (i.e., tree-level, bit-plane level, and byte-level) parallel-set partitioning in hierarchical trees (TP-SPIHT) coding on a collaborative central and graphic processing unit (CPU and GPU) to parallelize SPIHT by optimizing its dynamic processing with the linked list. Basic parallel SPIHT coding is presented with preprocessing, tree-level parallel coding, and bit-stream organization using three kinds of static marker matrices instead of the dynamic linked lists originally used to remove the data dependency of the original SPIHT. The bit-stream organization is implemented on CPU and other processes are implemented on GPU using GPU streams. The bit-stream organization is further divided into a bit-plane level parallel bit-plane stream extraction and a final bit-stream organization on a multicore CPU. Because no dependencies exist between the different byte operations in the final bit-stream organization, this organization is accelerated by byte-level parallelization on the GPU. Experimental results with different sized RSIs show that TP-SPIHT takes 292.03 ms to code a 2048×2048 image and achieves a 6.27 times speedup compared with an optimized CPU implementation. The speedup ratio improves as the image increases from 256×256 to 2048×2048.

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Hao Chen, Anqi Wei, and Ye Zhang "Three-level parallel-set partitioning in hierarchical trees coding based on the collaborative CPU and GPU for remote sensing images compression," Journal of Applied Remote Sensing 11(4), 045015 (18 December 2017). https://doi.org/10.1117/1.JRS.11.045015

Received: 19 June 2017; Accepted: 15 November 2017; Published: 18 December 2017

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