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Embedded foveation image coding.

Z Wang1, A C Bovik

  • 1Dept. of Electr. and Comput. Eng., Texas Univ., Austin, TX 78712-1084, USA. zwang@ece.utexas.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
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This study introduces an embedded foveation image coding (EFIC) algorithm that optimizes visual quality by leveraging the human visual system's (HVS) space-variant resolution. EFIC enhances image compression by prioritizing detail in the foveated region for better perceptual quality.

Area of Science:

  • Computer Vision
  • Image Processing
  • Human Visual System Modeling

Background:

  • The human visual system (HVS) exhibits space-variant resolution, with highest acuity at the foveation point and decreasing resolution with eccentricity.
  • Embedded wavelet image coding algorithms like EZW and SPIHT offer good compression and scalability but do not explicitly optimize for foveated visual quality.
  • Exploiting the HVS's space-variant nature allows for efficient image compression by reducing redundancy in peripheral visual areas.

Purpose of the Study:

  • To propose a novel embedded foveation image coding (EFIC) algorithm designed to optimize perceived image quality based on the HVS's spatial resolution characteristics.
  • To integrate foveation filtering and coding strategies to enhance visual quality at the bitstream level for arbitrary bit-rates.
  • To develop and utilize a foveation-based image quality metric, the foveated wavelet image quality index (FWQI), for evaluating coding performance.

Related Experiment Videos

Main Methods:

  • Development of the embedded foveation image coding (EFIC) algorithm, which orders the encoded bitstream to prioritize visual information relevant to the foveated region.
  • Implementation of a foveation-based image quality metric (FWQI) to guide the coding process and assess perceptual quality.
  • Modification of the Set Partitioning in Hierarchical Trees (SPIHT) algorithm to improve coding efficiency within the EFIC framework.

Main Results:

  • The proposed EFIC algorithm effectively integrates foveation filtering with image coding principles.
  • Experimental results demonstrate superior coding performance and scalability compared to existing methods when evaluated using the FWQI metric.
  • EFIC achieves perceptually optimized image quality by adapting the bitstream to the space-variant nature of human vision.

Conclusions:

  • EFIC provides a significant advancement in image compression by directly optimizing for foveated visual quality.
  • The algorithm's ability to adapt to arbitrary bit-rates and its strong performance metrics validate its effectiveness.
  • This approach offers a promising direction for image coding systems that aim to mimic and leverage the capabilities of the human visual system.