@inproceedings{eprints3481,
          author = {Massimo Minervini and Sotirios A. Tsaftaris},
            year = {2015},
           title = {Classification-aware distortion metric for HEVC intra coding},
           month = {December},
       booktitle = {2015 Visual Communications and Image Processing (VCIP)},
       publisher = {IEEE},
             url = {http://eprints.imtlucca.it/3481/},
        abstract = {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.},
        keywords = {Bit rate;Distortion;Encoding;Image coding;Optimization;Standards}
}