Distributed visual analysis applications, such as mobile visual search or
Visual Sensor Networks (VSNs) require the transmission of visual content on a
bandwidth-limited network, from a peripheral node to a processing unit.
Traditionally, a Compress-Then-Analyze approach has been pursued, in which
sensing nodes acquire and encode the pixel-level representation of the visual
content, that is subsequently transmitted to a sink node in order to be
processed. This approach might not represent the most effective solution, since
several analysis applications leverage a compact representation of the content,
thus resulting in an inefficient usage of network resources. Furthermore,
coding artifacts might significantly impact the accuracy of the visual task at
hand. To tackle such limitations, an orthogonal approach named
Analyze-Then-Compress has been proposed. According to such a paradigm, sensing
nodes are responsible for the extraction of visual features, that are encoded
and transmitted to a sink node for further processing. In spite of improved
task efficiency, such paradigm implies the central processing node not being
able to reconstruct a pixel-level representation of the visual content. In this
paper we propose an effective compromise between the two paradigms, namely
Hybrid-Analyze-Then-Compress (HATC) that aims at jointly encoding visual
content and local image features. Furthermore, we show how a target tradeoff
between image quality and task accuracy might be achieved by accurately
allocating the bitrate to either visual content or local features.Comment: submitted to IEEE International Conference on Image Processin
