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.
Lossy image video codecs, such as JPEG and High Efficiency Video Coding (HEVC) @cite_5 , give a profound impact on image video compression. Considering the recent success of neural network-based methods in various domains, a stream of CNN-based improvements for these codecs have been proposed to further increase coding efficiency. Most of these works focus on enhancing the performance @cite_1 @cite_25 or reducing the complexity @cite_46 @cite_32 of codec by replacing manually designed function with learning-based approach. Similarly, a series of works adopt CNN in post-processing to reduce the artifacts of compression @cite_18 @cite_36 @cite_9 . Encouraged by positive results in domain of super-resolution, another line of work encodes the down-sampled content with codec and then up-samples the decoded one by CNN for reconstruction @cite_7 @cite_19 .
@cite_18 @cite_7 @cite_36 @cite_9 @cite_1 @cite_32 @cite_19 @cite_5 @cite_46 @cite_25
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Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, post-processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG compressed images. In this paper, we present a CNN-based post-processing algorithm for High Efficiency Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post-processing leads to on average 4.6 bit-rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit-rate reduction, lower memory cost, and multiplied computational speedup. The High Efficiency Video Coding (HEVC) standard significantly saves coding bit-rate over the proceeding H.264 standard, but at the expense of extremely high encoding complexity. In fact, the coding tree unit (CTU) partition consumes a large proportion of HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, we propose in this paper a complexity reduction approach for intra-mode HEVC, which learns a deep convolutional neural network (CNN) model to predict CTU partition instead of RDO. Firstly, we establish a large-scale database with diversiform patterns of CTU partition. Secondly, we model the partition as a three-level classification problem. Then, for solving the classification problem, we develop a deep CNN structure with various sizes of convolutional kernels and extensive trainable parameters, which can be learnt from the established database. Finally, experimental results show that our approach reduces intramode encoding time by 62.25 and 69.06 with negligible Bj⊘ntegaard delta bit-rate of 2.12 and 1.38 , over the test sequences and images respectively, superior to other state-of-the-art approaches. Inspired by the recent advances of image super-resolution using convolutional neural network (CNN), we propose a CNN-based block up-sampling scheme for intra frame coding. A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution. Different from previous studies on down up-sampling-based coding, the up-sampling methods in our scheme have been designed by training CNN instead of hand-crafted. We explore a new CNN structure for up-sampling, which features deconvolution of feature maps, multi-scale fusion, and residue learning, making the network both compact and efficient. We also design different networks for the up-sampling of luma and chroma components, respectively, where the chroma up-sampling CNN utilizes the luma information to boost its performance. In addition, we design a two-stage up-sampling process, the first stage being within the block-by-block coding loop, and the second stage being performed on the entire frame, so as to refine block boundaries. We also empirically study how to set the coding parameters of down-sampled blocks for pursuing the frame-level rate-distortion optimization. Our proposed scheme is implemented into the high-efficiency video coding (HEVC) reference software, and a comprehensive set of experiments have been performed to evaluate our methods. Experimental results show that our scheme achieves significant bits saving compared with the HEVC anchor, especially at low bit rates, leading to on average 5.5 BD-rate reduction on common test sequences and on average 9.0 BD-rate reduction on ultrahigh definition test sequences. Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened images that are accompanied with ringing effects. Inspired by the success of deep convolutional networks (DCN) on superresolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. To meet the speed requirement of real-world applications, we further accelerate the proposed baseline model by layer decomposition and joint use of large-stride convolutional and deconvolutional layers. This also leads to a more general CNN framework that has a close relationship with the conventional Multi-Layer Perceptron (MLP). Finally, the modified network achieves a speed up of 7.5 times with almost no performance loss compared to the baseline model. We also demonstrate that a deeper model can be effectively trained with features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate three practical transfer settings and show the effectiveness of transfer learning in low-level vision problems. Our method shows superior performance than the state-of-the-art methods both on benchmark datasets and a real-world use case.