Google Inc.

Mohammad Izadi

Balineedu Adsumilli

Neil Birkbeck

Damien Kelly

Yeping Su

Anil Christopher Kokaram

Pavan Madhusudanarao

Jeremy Dorfman

Balineedu C Adsumilli

Adaptive filtering is used video stream for bitrate reduction. A first copy of the input video stream is encoded to a reference bitstream. Each of a number of candidate filters is applied to each frame of a second copy of the input video stream to produce a filtered second copy of the input video stream. The filtered second copy is encoded to a candidate bitstream. A cost value for the candidate filter is determined based on distortion value and bitrate differences between the candidate bitstream and the reference bitstream. The candidate bitstream corresponding to the candidate filter with a lowest one of the cost values is selected as the output bitstream, which is then output or stored. Processing the input video stream using the adaptive filter and before the encoding may result in bitrate reduction, thereby improving compression, decompression, and other performance.

Video stream adaptive filtering for bitrate reduction

Image data is processed for noise reduction before encoding and subsequent decoding. For an input image in a spatial domain, two-dimensional (2-D) wavelet coefficients at multiple levels are generated. Each level includes multiple subbands, each associated with a respective subband type in a wavelet domain. For respective levels, a flat region of a subband is identified, which flat region includes blocks of the subband having a variance no higher than a first threshold variance. A flat block set for the subband type associated with the subband is identified, which includes blocks common to respective flat regions of the subband. A second threshold variance is determined using variances of the flat block set, and is then used for thresholding at least some of the 2-D wavelet coefficients to remove noise. After thresholding, a denoised image is generated in the spatial domain using the levels.

Adaptive wavelet denoising

Image denoising includes obtaining a saliency map for an image. The saliency map includes respective saliency scores for pixels of the image. Respective noise levels are assigned to the pixels using the respective saliency scores to obtain a noise level map. The image is denoised using the noise level map to obtain a denoised image. The denoised image is output, such as to a display or a storage device.

Saliency based denoising

Processing a spherical video using denoising is described. Video content comprising the spherical video is received. Whether a camera geometry or a map projection, or both, used to generate the spherical video is available is then determined. The spherical video is denoised using a first technique responsive to a determination that the camera geometry, the map projection, or both is available. Otherwise, the spherical video is denoised using a second technique. At least some steps of the second technique can be different from steps of the first technique. The denoised spherical video can be encoded for transmission or storage using less data than encoding the spherical video without denoising.

Method for denoising omnidirectional videos and rectified videos

Denoising video content includes identifying a three-dimensional flat frame block of multiple frames of the video content, wherein the three-dimensional flat frame block comprises flat frame blocks, each flat frame block is located within a respective frame of the multiple frames, and the flat frame blocks have a spatial and temporal intensity variance that is less than a threshold. Denoising video content also includes determining an average intensity value of the three-dimensional flat frame block, determining a noise model that represents noise characteristics of the three-dimensional flat frame block, generating a denoising function using the average intensity value and the noise model, and denoising the multiple frames using the denoising function.

Noise reduction method for high dynamic range videos

A method for denoising video content includes identifying a first frame block associated with a first frame of the video content. The method also includes estimating a first noise model that represents characteristics of the first frame block. The method also includes identifying at least one frame block adjacent to the first frame block. The method also includes generating a second noise model that represents characteristics of the at least one frame block adjacent to the first frame block by adjusting the first noise model based on at least one characteristic of the at least one frame block adjacent to the first frame block. The method also includes denoising the at least one frame block adjacent to the first frame block using the second noise model.

Method and apparatus for denoising video content

A method for denoising video content includes identifying a first frame block of a plurality of frame blocks associated with a first frame of the video content. The method also includes determining an average intensity value for the first frame block. The method also includes determining a first noise model that represents characteristics of the first frame block. The method also includes generating a denoising function using the average intensity value and the first noise model for the first frame block. The method further includes denoising the plurality of frame blocks using the denoising function.

Implementations disclose mutual noise estimation for videos. A method includes determining an optimal frame noise variance for intensity values of each frame of frames of a video, the optimal frame noise variance based on a determined relationship between spatial variance and temporal variance of the intensity values of homogeneous blocks in the frame, identifying an optimal video noise variance for the video based on optimal frame noise variances of the frames of the video, selecting, for each frame of the video, one or more of the blocks having a spatial variance that is less than the optimal video noise variance, the one or more frames selected as the homogeneous blocks, and utilizing the selected homogeneous blocks to estimate a noise signal of the video.

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San Jose, CA, United States of America

Mohammad Izadi