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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]

Blind image deblurring aims to obtain a clear image and blur kernel from a blurred image. Most existing methods estimate the blur kernel through the entire image. However, different image information, such as image structure information, smooth area information and noise information, contribute differently to blur kernel estimation. The uniform processing of various image information will reduce the accuracy of blur kernel estimation. In this paper, we propose a new blind deblurring method based on the content-weighted data fidelity term, which can focus more on the sharp edge to restore image structure. Moreover, we construct a new image prior to constrain the weight matrix. However, the content-weighted data fidelity term is a non-convex function. In this work, we introduce the variable splitting method to replace content-weighted matrix, which can be optimized by alternating iteration method. A large number of experiments show that the proposed deblurring algorithm can obtain the best performance on natural images and text images. • A new adaptive content-weighted method is proposed to restore image structure. • A new image prior is proposed to constrain the weight matrix and solution space. • Solved the content-weighted data fidelity term through the variable splitting method. [ABSTRACT FROM AUTHOR]

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures. [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]

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]