Computational neuroscience has made substantial progress during the past three decades in better understanding the internal representation of the sensory world. Based on those results, it is our conviction that the mammalians visual system developed efficient coding strategies that could be used as a source of inspiration to imagine novel image and video compression algorithms. Our work on bio-inspired image coding focused on:

  • Design of a retina-inspired image coding scheme. During Khaled Masmoudi’s PhD (2009–2012) in collaboration with INRIA Sophia Antipolis Méditerranée, we developed a bio-inspired codec for images mainly based on a static approximation of the transformation performed by the outer layers of the retina. We proposed to solve the problem of reconstruction in an original approach by using the frames theory. We also investigated the issue of non-determinism in the retina neural code and proposed to model the retinal noise by a multiscale dither signal with specific statistical properties. The proposed coder gained interesting perceptual features that makes it competitive with the well established JPEG and JPEG 2000 standards. 
  • Development of a dynamic retina inspired filtering. Effrosyni Doutsi’s PhD (in progress since 2013) in collaboration with 4G-SGME in Sophia Antipolis proposed a retina-inspired filter based on a realistic mathematical model of the retina taking into account its dynamic behavior. It is shown that, while time increases, the retina-inspired spectrum varies from a lowpass filter to a bandpass filter. Hence, the retinainspired filter can increase the quality of the image by extracting dynamically more details. Based on the frame theory, we proved that the retina-inspired filter is invertible and we are able to reconstruct the input image after filtering.
  • Statistical detection and classification based on time-encoding. In many ways, computers today are nothing more than number crunchers and information manipulators. They all adhere to the Von Neumann architecture. Hence a new computing model is needed to process unstructured data such as images and video. We have been exploring the relevance of time-encoding: data are encoded as asynchronous pulses. This is typically how the human brain processes information. The exploratory projects COBRA and ENCODIME allowed us to publish some promising results on statistical estimation and classification based on time-encoded signals.