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- Read more about Low Complexity Joint RDO of Prediction Units Couples for HEVC Intra Coding
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HEVC is the latest block-based video compression standard, outperforming H.264/AVC by 50% bitrate savings for the same perceptual quality. An HEVC encoder provides Rate-Distortion optimization coding tools for block-wise compression. Because of complexity limitations, Rate-Distortion Optimization (RDO) is usually performed independently for each block, assuming coding efficiency losses to be negligible.
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- Read more about Autoencoder-based image compression: can the learning be quantization independent?
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- Read more about LEARNING-BASED COMPLEXITY REDUCTION AND SCALINGFOR HEVC ENCODERS
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- Read more about A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION
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In this paper, residual redundancy in compressed videos is exploited to alleviate transmission errors using joint source channel arithmetic decoding. A new method is proposed to estimate a priori probability in MAP metric of H.264 intra modes decoder. The decoder generates a decoding tree using a breadth first search algorithm. An introduced statistical model is then implemented stage by stage over the decoding tree.
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- Read more about FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING
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This paper presents a fast 3D-High Efficiency Video Coding (3D-HEVC) depth maps intra-frame prediction based on static Coding Unit (CU) splitting decisions trees. This coding approach uses data mining to extract the correlation among the encoder context attributes and to define a split decision tree for each CU level of the depth maps encoding. The decision trees were trained using the information extracted from 3D-HEVC Test Model (3D-HTM) and using the Common Test Conditions (CTC).
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- Read more about CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM
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Point cloud has attracted more and more attention in 3D object representation, especially in free-view rendering. However, it is challenging to efficiently deploy the point cloud due to its huge data amount with multiple attributes including coordinates, normal and color. In order to represent point clouds more compactly, we propose a novel point cloud compression method for attributes, based on geometric clustering and Normal Weighted Graph Fourier Transform (NWGFT).
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- Read more about An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
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The compressed sensing (CS) has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been proposed and obtained superior performance. However, these methods suffer from blocking artifacts or ringing effects at low sampling ratios in most cases. To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network.
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- Read more about OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION
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Test Zone Search (TZS) is considered the current state-of-the-art fast Motion Estimation algorithm because it presents
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- Read more about FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC
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- Read more about GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS
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In this paper, we explore the use of graph-basedtransforms to capture correlation in light fields. We consider a scheme in which view synthesis is used as a first step to exploit inter-view correlation. Local graph-based transforms (GT) are then considered for energy compaction of the residue signals. The structure of the local graphs is derived from a coherent super-pixel over-segmentation of the different views. The GT is computed and applied in a separable manner with a first spatial unweighted transform followed by an inter-view GT.
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