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.
Every user of multimedia technology expects good image and video visual quality independently of the particular characteristics of the receiver or the communication networks employed. Unfortunately, due to factors like processing power limitations and channel capabilities, images or video sequences are often downsampled and or transmitted or stored at low bitrates, resulting in a degradation of their final visual quality. In this paper, we propose a region-based framework for intentionally introducing downsampling of the high resolution (HR) image sequences before compression and then utilizing super resolution (SR) techniques for generating an HR video sequence at the decoder. Segmentation is performed at the encoder on groups of images to classify their blocks into three different types according to their motion and texture. The obtained segmentation is used to define the downsampling process at the encoder and it is encoded and provided to the decoder as side information in order to guide the SR process. All the components of the proposed framework are analyzed in detail. A particular implementation of it is described and tested experimentally. The experimental results validate the usefulness of the proposed method. The term super-resolution is typically used in the literature to describe the process of obtaining a high resolution (HR) image or a sequence of HR images from a set of low resolution (LR) observations. This term has been applied primarily to spatial and temporal resolution enhancement. However, intentional pre-processing and downsampling can be applied during encoding and super-resolution techniques to upsample the image can be applied during decoding when video compression is the main objective. In this paper we consider the following three video compression models. The first one simply compresses the sequence using any of the available standard compression methods, the second one pre-processes (without downsampling) the image sequence before compression, so that post-processing (without upsampling) is applied to the compressed sequence. The third model includes downsampling in the pre-processing stage and the application of a super resolution technique during decoding. In this paper we describe these three models but concentrate on the application of super-resolution techniques as a way to post-process and upsample a compressed video sequences. Experimental results are provided on a wide range of bitrates for two very important applications: format conversion between different platforms and scalable video coding. It has been reported that oversampling a still image before compression does not guarantee a good image quality. Similarly, down-sampling before video compression in low bit rate video coding may alleviate the blocking effect and improve peak signal-to-noise ratio of the decoded frames. When the number of discrete cosine transform coefficients is reduced in such a down-sampling based coding (DBC), the bit budget of each coefficient will increase, thus reduce the quantization error. A DBC video coding scheme is proposed in this paper, where a super-resolution technique is employed to restore the down-sampled frames to their original resolutions. The performance improvement of the proposed DBC scheme is analyzed at low bit rates, and verified by experiments. Super-resolution algorithms recover high-frequency information from a sequence of low-resolution observations. In this paper, we consider the impact of video compression on the super-resolution task. Hybrid motion-compensation and transform coding schemes are the focus, as these methods provide observations of the underlying displacement values as well as a variable noise process. We utilize the Bayesian framework to incorporate this information and fuse the super-resolution and post-processing problems. A tractable solution is defined, and relationships between algorithm parameters and information in the compressed bitstream are established. The association between resolution recovery and compression ratio is also explored. Simulations illustrate the performance of the procedure with both synthetic and nonsynthetic sequences.