Abstract: Systems and methods are disclosed for determining the perceptibility of noise in a block of images and/or video. The systems and methods may compute a mask value for the block using a block masking generator. The mask value may indicate the perceptibility of noise in the block. The mask value may be computed using a normalized activity value and/or a texture value for the block. The normalized activity value may indicate the relative activity in the block as compared to the activity in the image and/or video. The texture value may indicate the strength and/or number of edges in the block.

Abstract: Systems and methods are disclosed for determining the perceptibility of noise in a block of images and/or video. The systems and methods may compute a mask value for the block using a block masking generator. The mask value may indicate the perceptibility of noise in the block. The mask value may be computed using a normalized activity value and/or a texture value for the block. The normalized activity value may indicate the relative activity in the block as compared to the activity in the image and/or video. The texture value may indicate the strength and/or number of edges in the block.

Abstract: A frame level noise estimate for an image can be determined. An image processor includes a high pass filter unit configured to perform high-pass spatial filtering of image data for first and second frames to produce high-pass spatially filtered information for the first frame and the second frame. A cumulative histogram generator is configured to analyze the high-pass spatially filtered information for the first frame and the second frame to produce a first cumulative histogram for the first frame and a second cumulative histogram for the second frame. A comparator is configured to determine a difference value between the first and second cumulative histograms. A mapping unit is configured to determine an estimated noise value based on the difference value.