One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis synthesis, binarization, etc. Experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding.
In the works of @cite_11 @cite_41 @cite_29 @cite_42 , a discrete and compact representation is obtained by applying a quantization to the bottleneck of auto-encoder. To achieve variable bit rates, the model progressively analyzes and synthesizes residual errors with several auto-encoders. Progressive codes are essential to Rate-Distortion Optimization (RDO), since a higher quality can be attained by adding additional bits. On the basis of these progressive analyzer, Baig introduce an inpainting scheme that exploits spatial coherence exhibited by neighboring blocks to reduce redundancy in image @cite_38 .
@cite_38 @cite_41 @cite_29 @cite_42 @cite_11
2013294225 2017801928 2113905985 2109053700
This paper presents a rate-distortion (RD) optimized interactive streaming method for multiview video pre-compressed by H.264 Joint Multiview Video Model (JMVM). In the proposed method, multiple encodings are first used to facilitate the flexible server-client interaction. Second, a RD-optimized scheduling strategy is provided to guarantee the optimal view-dependent delivery of multiview video. In the RD-optimized scheduling strategy, a distortion model is proposed to estimate the expected end-to-end distortion by accounting for both coding and packet-loss-induced distortions, as well as rendering-induced distortion. With the end-to-end distortion model, the server can select the optimal encoding combination for transmission. Experimental results demonstrate that the proposed method can achieve a significant end-to-end RD performance improvement over the selective streaming methods with simulcast coding or scalable multiview coding. In addition, it has better error-resilience performance to combat with packet-losses over the Internet protocol (IP) networks. This paper provides a technical overview of a newly added in-loop filtering technique, sample adaptive offset (SAO), in High Efficiency Video Coding (HEVC). The key idea of SAO is to reduce sample distortion by first classifying reconstructed samples into different categories, obtaining an offset for each category, and then adding the offset to each sample of the category. The offset of each category is properly calculated at the encoder and explicitly signaled to the decoder for reducing sample distortion effectively, while the classification of each sample is performed at both the encoder and the decoder for saving side information significantly. To achieve low latency of only one coding tree unit (CTU), a CTU-based syntax design is specified to adapt SAO parameters for each CTU. A CTU-based optimization algorithm can be used to derive SAO parameters of each CTU, and the SAO parameters of the CTU are inter leaved into the slice data. It is reported that SAO achieves on average 3.5 BD-rate reduction and up to 23.5 BD-rate reduction with less than 1 encoding time increase and about 2.5 decoding time increase under common test conditions of HEVC reference software version 8.0. We introduce a new image compression paradigm that combines compression efficiency with speed, and is based on an independent "infinite" mixture model which accurately captures the space-frequency characterization of the wavelet image representation. Specifically, we model image wavelet coefficients as being drawn from an independent generalized Gaussian distribution field, of fixed unknown shape for each subband, having zero mean and unknown slowly spatially-varying variances. Based on this model, we develop a powerful "on the fly" estimation-quantization (EQ) framework that consists of: (i) first finding the maximum-likelihood estimate of the individual spatially-varying coefficient field variances based on causal and quantized spatial neighborhood contexts; and (ii) then applying an off-line rate-distortion (R-D) optimized quantization entropy coding strategy, implemented as a fast lookup table, that is optimally matched to the derived variance estimates. A distinctive feature of our paradigm is the dynamic switching between forward and backward adaptation modes based on the reliability of causal prediction contexts. The performance of our coder is extremely competitive with the best published results in the literature across diverse classes of images and target bitrates of interest, in both compression efficiency and processing speed. For example, our coder exceeds the objective performance of the best zerotree-based wavelet coder based on space-frequency-quantization at all bit rates for all tested images at a fraction of its complexity. Consider a pair of correlated Gaussian sources (X 1,X 2). Two separate encoders observe the two components and communicate compressed versions of their observations to a common decoder. The decoder is interested in reconstructing a linear combination of X 1 and X 2 to within a mean-square distortion of D. We obtain an inner bound to the optimal rate-distortion region for this problem. A portion of this inner bound is achieved by a scheme that reconstructs the linear function directly rather than reconstructing the individual components X 1 and X 2 first. This results in a better rate region for certain parameter values. Our coding scheme relies on lattice coding techniques in contrast to more prevalent random coding arguments used to demonstrate achievable rate regions in information theory. We then consider the case of linear reconstruction of K sources and provide an inner bound to the optimal rate-distortion region. Some parts of the inner bound are achieved using the following coding structure: lattice vector quantization followed by ldquocorrelatedrdquo lattice-structured binning.