Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric

Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively

RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics

Image inpainting refers to filling missing places in images using neighboring pixels. It also has many applications in different tasks of image processing. Most of these applications enhance the image quality by significant unwanted changes or even elimination of some existing pixels. These changes require considerable computational complexities which in turn results in remarkable processing time. In this paper we propose a fast inpainting algorithm called RIBBONS based on selection of patches around each missing pixel. This would accelerate the execution speed and the capability of online frame inpainting in video. The applied cost-function is a combination of statistical and spatial features in all neighboring pixels. We evaluate some candidate patches using the proposed cost function and minimize it to achieve the final patch. Experimental results show the higher speed of 'Ribbons' in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset

Chebyshev and Conjugate Gradient Filters for Graph Image Denoising

In 3D image/video acquisition, different views are often captured with varying noise levels across the views. In this paper, we propose a graph-based image enhancement technique that uses a higher quality view to enhance a degraded view. A depth map is utilized as auxiliary information to match the perspectives of the two views. Our method performs graph-based filtering of the noisy image by directly computing a projection of the image to be filtered onto a lower dimensional Krylov subspace of the graph Laplacian. We discuss two graph spectral denoising methods: first using Chebyshev polynomials, and second using iterations of the conjugate gradient algorithm. Our framework generalizes previously known polynomial graph filters, and we demonstrate through numerical simulations that our proposed technique produces subjectively cleaner images with about 1-3 dB improvement in PSNR over existing polynomial graph filters.Comment: 6 pages, 6 figures, accepted to 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW

Affective Sustainability. The Creation and Transmission of Affect through an Educative Process: An Instrument for the Construction of more Sustainable Citizens

Although for many years the debate on sustainability has focused on the generation of critical thinking based on the dynamic balance between the economic, social and environmental spheres, in the following text we propose to elaborate on the use of a eminently human condition, such as the capacity to love and create an emotional attachment, whether with our environment or our fellow men, as an initiator and main force for change to the building a more sustainable model of development. To do so we shall begin from the concept coined by Adriana Bisquert in the 90s, that is A ective sustainability, by analyzing it, delving into its possible definitions by means of the development of the project for Environmental Education and Development called “Educating for a more sustainable citizenship” undertaken by the Spanish NGO (non-governmental organization) or ITACA Ambiente Elegido, and developed in the locality of Paterna de Rivera, Cádiz (Spain). This is a practical and real example, which is used to establish a work educational methodology that enables us to consider this concept as the real basis for an exportable and replicable work in a painstaking search for the creation of a more sustainable city