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Deep convolutional neural network based image super-resolution (SR) models have shown superior performance in recovering the underlying high resolution (HR) images from low resolution (LR) images obtained from the predefined downscaling methods. In this paper, we propose a learned image downscaling method based on content adaptive resampler (CAR) with consideration on the upscaling process. The proposed resampler network generates content adaptive image resampling kernels that are applied to the original HR input to generate pixels on the downscaled image. Moreover, a differentiable upscaling (SR) module is employed to upscale the LR result into its underlying HR counterpart. By back-propagating the reconstruction error down to the original HR input across the entire framework to adjust model parameters, the proposed framework achieves a new state-of-the-art SR performance through upscaling guided image resamplers which adaptively preserve detailed information that is essential to the upscaling. Experimental results indicate that the quality of the generated LR image is comparable to that of the traditional interpolation based method and the significant SR performance gain is achieved by deep SR models trained jointly with the CAR model. The code is publicly available on: https://github.com/sunwj/CAR. [ABSTRACT FROM AUTHOR]

In this paper, a fast intra prediction algorithm for High Efficiency Video Coding (HEVC) is proposed by adopting the mode correlation between adjacent coding units. The proposed approach is realized by two pieces. Firstly, the prediction mode and partition structure for the current coding unit will inherit its optimal prediction coding unit directly if the Structural Similarity (SSIM) between the adjacent coding units satisfies the threshold condition. Secondly, unless the first case occurs, a two-step rough mode decision with adaptive threshold method including Sum of Absolute Difference (SAD) and Sum of Absolute Hadamard Transformed Difference (SATD) criterions is used to accelerate intra mode decision process based on HEVC test Model (HM) encoder. Besides, the most probable mode (MPM) which refers to the optimal prediction mode of the coded left and upper Prediction Units (PUs) surrounding the current PU is also considered. Experimental results show that the proposed algorithm can save 30.18 % encoding time on average with negligible loss of coding efficiency compared with HM10.0. The complexity and the bitrate are reduced by 11.45 and 0.46 % respectively compared with coding tree pruning method. [ABSTRACT FROM AUTHOR]

Stereo interleaving video coding, in which both left and right view frames are subsampled into half size and multiplexed into one single frame before being encoded by a traditional 2-D video encoder, is an efficient encoding scenario for stereoscopic video. Many existing stereo interleaving video coding methods subsample each frame by utilizing fixed subsampling filter coefficients. Such methods are easy to implement; however, the varying property of the frame signal is ignored. By jointly considering the influences of subsampling and compression, a rate and distortion analysis about stereo interleaving video coding is proposed. The final distortion in stereo interleaving video coding is the summation of errors caused by subsampling (causing distortion between subsampling-interpolated image and the original full resolution one) and by quantization during compression. Based on the provided rate distortion analysis, a content adaptive image subsampling (CAIS) is also proposed. In CAIS, the half-size frames are generated by the optimal subsampling filters, which are calculated based on frame contents and the targeted interpolation coefficients. Experimental results demonstrate that the proposed CAIS is able to greatly improve compression efficiency of stereo interleaving video coding. [ABSTRACT FROM AUTHOR]

With advances in broadcasting technologies, people are now able to watch videos on devices such as televisions, computers, and mobile phones. Scalable video provides video bitstreams of different size under different transmission bandwidths. In this paper, a semantic scalability scheme with four levels is proposed, and tennis videos are used as examples in experiments to test the scheme. Rather than detecting shot categories to determine suitable scaling options for Scalable Video Coding (SVC) as in previous studies, the proposed method analyzes a video, transmits video content according to semantic priority, and reintegrates the extracted contents in the receiver. The purpose of the lower bitstream size in the proposed method is to discard video content of low semantic importance instead of decreasing the video quality to reduce the video bitstream. The experimental results show that visual quality is still maintained in our method despite reducing the bitstream size. Further, in a user study, we show that evaluators identify the visual quality as more acceptable and the video information as clearer than those of SVC. Finally, we suggest that the proposed scalability scheme in the semantic domain, which provides a new dimension for scaling videos, can be extended to various video categories. [ABSTRACT FROM AUTHOR]

The H.264/AVC video coding standard achieves a high coding efficiency compared to previous standards. However, the encoder complexity results in a very high computational cost due to motion estimation and macroblock mode decisions. In this paper, we propose a simple, content adaptive mode decision method suitable for a variety of applications. Experimental results show that the proposed algorithm effectively reduces about 60% of the encoding time with negligible quality loss in respect to the reference model and it compares favorably with two of the most recognized algorithms in the literature. [ABSTRACT FROM AUTHOR]