Abstract:
A superior Color Transient Improvement technique is adaptive to the local image features, so that more natural color edge transition improvement can be accomplished. A gain control function is provided that depends on the local image feature so that different regions of the image can be treated differently. Further, a correction signal is controlled in such a way (by the local image feature) that neither undershoot nor overshoot occurs, eliminating the need for post-processing for undershoot/overshoot removal.
Abstract:
An improved noise reduction process by wavelet thresholding utilizes a discrete wavelet transform to decompose the image into different resolution levels. A thresholding function is then applied in different resolution levels with different threshold values to eliminate insignificant wavelet coefficients which mainly correspond to the noise in the original image. Finally, an inverse discrete wavelet transform is applied to generate the noise-reduced video image. The threshold values are based on the relationships between the noise standard deviations of different decomposition levels in the wavelet domain and the noise standard deviation of the original image.
Abstract:
A ringing area detector classifies the input image into two regions: a mosquito noise region (i.e. filtering region) and a non-mosquito noise region (i.e. non-filtering region), and uses this classification information to adaptively remove the mosquito noise in a mosquito noise reduction system. The mosquito noise reduction system includes a ringing area detector, a local noise power estimator, a smoothing filter, and a mixer. The ringing area detector includes an edge detector, a near edge detector, a texture detector, and a filtering region decision block. The ringing detection block detects the ringing area where the smoothing filter is to be applied. The local noise power estimator controls the filter strength of the smoothing filter. The smoothing filter smoothes the input image. The mixer mixes the smoothed image and the original image properly based on the region information from the ringing area detection block.
Abstract:
A global and local statistics controlled noise reduction system in which the video image noise reduction processing is effectively adaptive to both image local structure and global noise level. A noise estimation method provides reliable global noise statistics to the noise reduction system. The noise reduction system dynamically/adaptively configures a local filter for processing each image pixel, and processes the pixel with that local filter. The filtering process of the noise reduction system is controlled by both global and local image statistics that are also computed by the system.
Abstract:
A video noise reduction system for reducing video noise in a sequence of video frames. In the video noise reduction system, a temporal filter computes multiple temporal average values for the video frames in different temporal directions. A motion detector computes multiple motion signal values for the video frames in different temporal directions. Finally, a control unit selects one of the temporal average values based on the motion signal values as output.
Abstract:
A video noise reduction system for a set of video frames that computes a first motion signal using a current frame and multiple consecutive previous frames, computes a second motion signal using the current frame and the processed preceding frame; computes the multi-frame temporal average of the current frame and multiple consecutive previous frames; computes the recursive average of the current frame and the processed preceding frame; generates a temporal filtered signal by soft switching between the multi-frame temporal average and the recursive average based on the first motion signal; applies a spatial filter to the current frame to generate a spatial filtered signal; and combines the temporal filtered signal and the spatial filtered signal based on the second motion signal to generate a final noise reduced video output signal.
Abstract:
A noise reduction system that not only preserves details in images but also provides essentially clean, smooth, and natural looking homogeneous regions in images. The noise reduction system utilizes a dual-channel adaptive noise reduction technique. The input signal is first split into two channels (i.e., a low-pass channel and a high-pass channel), by a channel splitting filter. Then the two channel signals are processed separately. The low-pass channel signal is processed using an adaptive directional filter based on the estimation of the local 2D and 1D statistics and the detection of the local image structure direction. The high-pass channel signal is processed by a non-linear filtering method based on the estimation of the local statistics and the noise level of the high-pass channel signal, which is derived from the noise level of the original input signal. The processed signals from the two channels are summed together to get the final output.
Abstract:
A global and local statistics controlled noise reduction system in which the video image noise reduction processing is effectively adaptive to both image local structure and global noise level. A noise estimation method provides reliable global noise statistics to the noise reduction system. The noise reduction system dynamically/adaptively configures a local filter for processing each image pixel, and processes the pixel with that local filter. The filtering process of the noise reduction system is controlled by both global and local image statistics that are also computed by the system.
Abstract:
A grid detector detects the existence and the location of grids in DCT compressed videos. When a grid is detected in the input video, a post-processor is turned on and the de-blocking processing is applied on the grid detected by the grid detector. When no grid is detected, indicating that the input video is either an uncompressed video or an already de-blocked video, post-processing turned off to avoid degrading the picture quality. To detect grids, the grid detector: (a) computes horizontal and vertical second derivatives for all pixels of the image; (b) generates horizontal second derivative zero-crossing mask and vertical second derivative zero-crossing mask by marking the those pixels whose second derivatives have opposite signs with respect to their horizontal or vertical neighboring pixels'; (c) applies horizontal and vertical integral projections to the horizontal and vertical zero-crossing masks respectively; (d) generates the local maximum masks by locating the local maximum of the two projected 1-D signals; and (e) determines grid location by computing the positions of the local maximum masks.
Abstract:
A video quality adaptive coding artifact reduction system has a video quality analyzer, an artifact reducer, and a filter strength controller. The video quality analyzer employs input video quality analysis to control artifact reduction. The video quality analyzer accesses the video quality of the decoded video sequence to estimate the input video quality. The filter strength controller globally controls the filter strength of the artifact reducer based on the video quality estimate by the video quality analyzer. For low quality input video, the filter strength controller increases the artifact reduction filter strength to more efficiently reduce the artifact. For high quality input video, the filter strength controller decreases the artifact reduction filter strength to avoid blurring image detail.