Towards Haptic Images: A Survey on Touchscreen-Based Surface Haptics.

The development of tactile screens opens new perspectives for co-located images and haptic rendering, leading to the concept of "haptic images." They emerge from the combination of image data, rendering hardware, and haptic perception. This enables one to perceive haptic feedback while manually exploring an image. This raises nevertheless two scientific challenges, which serve as thematic axes for the state of the art of this survey. Firstly, the choice of appropriate haptic data raises a number of issues about human perception, measurements, modeling and distribution. Secondly, the choice of appropriate rendering technology implies a difficult trade-off between expressiveness and usability.

Region-Based Prediction for Image Compression in the Cloud.

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

Extrafoveal Video Extension for an Immersive Viewing Experience.

Between the recent popularity of virtual reality (VR) and the development of 3D, immersion has become an integral part of entertainment concepts. Head-mounted Display (HMD) devices are often used to afford users a feeling of immersion in the environment. Another technique is to project additional material surrounding the viewer, as is achieved using cave systems. As a continuation of this technique, it could be interesting to extend surrounding projection to current television or cinema screens. The idea would be to entirely fill the viewer's field of vision, thus providing them with a more complete feeling of being in the scene and part of the story. The appropriate content can be captured using large field of view (FoV) technology, using a rig of cameras for 110 to 360 capture, or created using computergenerated images. The FoV is, however, rather limited in its use for existing (legacy) content, achieving between 36 to 90 degrees () field, depending on the distance from the screen. This paper seeks to improve this FoV limitation by proposing computer vision techniques to extend such legacy content to the peripheral (extrafoveal) vision without changing the original creative intent or damaging the viewer's experience. A new methodology is also proposed for performing user tests in order to evaluate the quality of the experience and confirm that the sense of immersion has been increased. This paper thus presents: i) an algorithm to spatially extend the video based on human vision characteristics, ii) its subjective results compared to state-of-the-art techniques, iii) the protocol required to evaluate the quality of the experience (QoE), and iv) the results of the user tests.

Scalable Image Coding Based on Epitomes.

In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that the significant improvement of the rate-distortion performances can be achieved compared with the Scalable extension of HEVC (SHVC).

A kinesthetic washout filter for force-feedback rendering.

Today haptic feedback can be designed and associated to audiovisual content (haptic-audiovisuals or HAV). Although there are multiple means to create individual haptic effects, the issue of how to properly adapt such effects on force-feedback devices has not been addressed and is mostly a manual endeavor. We propose a new approach for the haptic rendering of HAV, based on a washout filter for force-feedback devices. A body model and an inverse kinematics algorithm simulate the user's kinesthetic perception. Then, the haptic rendering is adapted in order to handle transitions between haptic effects and to optimize the amplitude of effects regarding the device capabilities. Results of a user study show that this new haptic rendering can successfully improve the HAV experience.

Correspondence map-aided neighbor embedding for image intra prediction.

This paper describes new image prediction methods based on neighbor embedding (NE) techniques. Neighbor embedding methods are used here to approximate an input block (the block to be predicted) in the image as a linear combination of K nearest neighbors. However, in order for the decoder to proceed similarly, the K nearest neighbors are found by computing distances between the known pixels in a causal neighborhood (called template) of the input block and the co-located pixels in candidate patches taken from a causal window. Similarly, the weights used for the linear approximation are computed in order to best approximate the template pixels. Although efficient, these methods suffer from limitations when the template and the block to be predicted are not correlated, e.g., in non homogenous texture areas. To cope with these limitations, this paper introduces new image prediction methods based on NE techniques in which the K-NN search is done in two steps and aided, at the decoder, by a block correspondence map, hence the name map-aided neighbor embedding (MANE) method. Another optimized variant of this approach, called oMANE method, is also studied. In these methods, several alternatives have also been proposed for the K-NN search. The resulting prediction methods are shown to bring significant rate-distortion performance improvements when compared to H.264 Intra prediction modes (up to 44.75% rate saving at low bit rates).