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Classification-aware distortion metric for HEVC intra coding

Minervini, Massimo and Tsaftaris, Sotirios A. Classification-aware distortion metric for HEVC intra coding. In: 2015 Visual Communications and Image Processing (VCIP), December 13-16, 2015, Singapore, Singapore (2015)

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Increasingly many vision applications necessitate the transmission of acquired images and video to a remote location for automated processing. When the image data are consumed by analysis algorithms and possibly never seen by a human, tailoring compression to the application is beneficial from a bit rate perspective. We inject prior knowledge of the application in the encoder to make rate-distortion decisions based on an estimate of the accuracy that will be achieved when analyzing reconstructed image data. Focusing on classification (e.g., used for image segmentation), we propose a new application-aware distortion metric based on a geometric interpretation of classification error. We devise an implementation for the High Efficiency Video Coding standard, and derive optimal model parameters for the A-domain rate control algorithm by curve fitting procedures. We evaluate our approach on time-lapse sequences from plant phenotyping experiments and cell fluorescence microscopy encoded in intra-only mode, observing a reduction in segmentation error across bit rates.

Item Type: Conference or Workshop Item (Paper)
Identification Number: 10.1109/VCIP.2015.7457877
Uncontrolled Keywords: Bit rate;Distortion;Encoding;Image coding;Optimization;Standards
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 05 May 2016 13:57
Last Modified: 05 May 2016 13:57
URI: http://eprints.imtlucca.it/id/eprint/3481

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