relation: http://eprints.imtlucca.it/3481/
title: Classification-aware distortion metric for HEVC intra coding
creator: Minervini, Massimo
creator: Tsaftaris, Sotirios A.
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: IEEE
date: 2015-12
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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)  
relation: http://dx.doi.org/10.1109/VCIP.2015.7457877
relation: 10.1109/VCIP.2015.7457877