7 January 2016 Low bit rates image compression via adaptive block downsampling and super resolution
Author Affiliations +
Abstract
A low bit rates image compression framework based on adaptive block downsampling and super resolution (SR) was presented. At the encoder side, the downsampling mode and quantization mode of each 16 × 16 macroblock are determined adaptively using the ratio distortion optimization method, then the downsampled macroblocks are compressed by the standard JPEG. At the decoder side, the sparse representation-based SR algorithm is applied to recover full resolution macroblocks from decoded blocks. The experimental results show that the proposed framework outperforms the standard JPEG and the state-of-the-art downsampling-based compression methods in terms of both subjective and objective comparisons. Specifically, the peak signal-to-noise ratio gain of the proposed framework over JPEG reaches up to 2 to 4 dB at low bit rates, and the critical bit rate to JPEG is raised to about 2.3 bits per pixel. Moreover, the proposed framework can be extended to other block-based compression schemes.
© 2016 SPIE and IS&T
Honggang Chen, Honggang Chen, Xiaohai He, Xiaohai He, Minglang Ma, Minglang Ma, Linbo Qing, Linbo Qing, Qizhi Teng, Qizhi Teng, } "Low bit rates image compression via adaptive block downsampling and super resolution," Journal of Electronic Imaging 25(1), 013004 (7 January 2016). https://doi.org/10.1117/1.JEI.25.1.013004 . Submission:
JOURNAL ARTICLE
10 PAGES


SHARE
RELATED CONTENT

Entropy-constrained predictive residual vector quantization
Proceedings of SPIE (December 07 1995)
Adaptive down-sampling video coding
Proceedings of SPIE (January 27 2010)
Classification and codebook design for a vector quantizer
Proceedings of SPIE (September 15 1994)
Channel-optimized transform coding of imagery
Proceedings of SPIE (July 20 1999)

Back to Top