Characterization and adaptive texture synthesis-based compression scheme

International audienceThis paper presents an adaptive texture synthesis-based compression scheme, where textured regions are detected and removed at encoder side, allowing the decoder to use texture synthesis to fill them. The detection relies on locally adaptive resolution segmentation. According to results shown by synthesis algorithms, they need to be parameterized according to the patterns to be synthesized. In this framework, the synthesizer gets its parameters from DCT feature-based texture descriptors. An adaptive pixel-based algorithm is used, relying on the comparison between current pixel neighborhood and those in an atypically shaped sample. Different neighborhood sizes are considered to better match texture patterns. The framework has been validated within an H.264/AVC video codec. Experimental results show significant bit-rate saving at similar visual quality

Texture refinement framework for improved video coding

ISBN: 978-0-8194-7936-5 - WOSInternational audienceH.264/AVC standard offers an efficient way of reducing the noticeable artefacts of former video coding schemes, but it can be perfectible for the coding of detailed texture areas. This paper presents a conceptual coding framework, utilizing visual perception redundancy, which aims at improving both bit-rate and quality on textured areas. The approach is generic and can be integrated into usual coding scheme. The proposed scheme is divided into three steps: a first algorithm analyses texture regions, with an eye to build a dictionary of the most representative texture sub-regions (RTS). The encoder preserves then them at a higher quality than the rest of the picture, in order to enable a refinement algorithm to finally spread the preserved information over textured areas. In this paper, we present a first solution to validate the framework, detailing then the encoder side in order to define a simple method for dictionary building. The proposed H.264/AVC compliant scheme creates a dictionary of macroblock

Local Inverse Tone Curve Learning for High Dynamic Range Image Scalable Compression

International audienceThis paper presents a scalable high dynamic range (HDR) image coding scheme in which the base layer is a lowdynamic range (LDR) version of the image that may have been generated by an arbitrary Tone Mapping Operator (TMO). No restriction is imposed on the TMO, which can be either global or local, so as to fully respect the artistic intent of the producer. Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template based Inter Layer Prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for blockwise ILP. In addition, this paper shows that a linear adjustment of the initially predicted block can further improve the overall coding performance by using an efficient encoding scheme of the scaling parameters. Our experiments have shown an average bitrate saving of 47% on the HDR enhancement layer, compared to previous local ILP methods

Clustering-based Methods for Fast Epitome Generation

International audienceThis paper deals with epitome generation, mainly dedicated here to image coding applications. Existing approaches are known to be memory and time consuming due to exhaustive self-similarities search within the image for each non-overlapping block. We propose here a novel approach for epitome construction that first groups close patches together. In a second time the self-similarities search is performed for each group. By limiting the number of exhaustive searches we limit the memory occupation and the processing time. Results show that interesting complexity reduction can be achieved while keeping a good epitome quality (down to 18.08 % of the original memory occupation and 41.39 % of the original processing time)

Learning Clustering-Based Linear Mappings for Quantization Noise Removal

International audienceThis paper describes a novel scheme to reduce the quantization noise of compressed videos and improve the overall coding performances. The proposed scheme first consists in clustering noisy patches of the compressed sequence. Then, at the encoder side, linear mappings are learned for each cluster between the noisy patches and the corresponding source patches. The linear mappings are then transmitted to the decoder where they can be applied to perform de-noising. The method has been tested with the HEVC standard, leading to a bitrate saving of up to 9.63%

Inter-prediction methods based on linear embedding for video compression

International audienceThis paper considers the problem of temporal prediction for inter-frame coding of video sequences using locally linear embedding (LLE). LLE-based prediction, first considered for intra-frame prediction, computes the predictor as a linear combination of K nearest neighbors (K-NN) searched within one or several reference frames. The paper explores different K-NN search strategies in the context of temporal prediction, leading to several temporal predictor variants. The proposed methods are tested as extra inter-frame prediction modes in an H.264 codec, but the proposed concepts are still valid in HEVC. The results show that significant rate-distortion performance gains are obtained with respect to H.264 (up to 15.31% bit-rate saving)

Scalable Light Field Compression Scheme Using Sparse Reconstruction and Restoration

International audienceThis paper describes a light field scalable compression scheme based on the sparsity of the angular Fourier transform of the light field. A subset of sub-aperture images (or views) is compressed using HEVC as a base layer and transmitted to the decoder. An entire light field is reconstructed from this view subset using a method exploiting the sparsity of the light field in the continuous Fourier domain. The reconstructed light field is enhanced using a patch-based restoration method. Then, restored samples are used to predict original ones, in a SHVC-based SNR-scalable scheme. Experiments with different datasets show a significant bit rate reduction of up to 24% in favor of the proposed compression method compared with a direct encoding of all the views with HEVC. The impact of the compression on the quality of the all-in-focus images is also analyzed showing the advantage of the proposed scheme

A Genetic Algorithm for the Automated Generation of Small Organic Molecules

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K-NN search using local learning based on regression for image prediction with neighbor embedding

International audienceThe paper describes a K-NN search method aided by local learning of subspace mappings for the problem of neighbor-embedding based image Intra prediction. The local learning of subspace mappings relies on multivariate linear regression. The method is used jointly with Locally Linear Embedding (LLE) as well as with a method inspired from Non Local Means (NLM) for prediction. Linear and kernel ridge regression are also considered directly for predicting the unknown pixels. Rate-distortion performances are then given in comparison with Intra prediction using LLE and classical K-NN search, as well as in comparison with H.264 Intra prediction modes

Quantitative structure-activity relationship studies of RAR α, β, γ retinoid agonists

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