Devices, systems and methods are disclosed for improving color resolution in YUV subsampled image signals. Chrominance data may be subsampled using two different techniques and the two different outputs may be interlaced to improve a resulting color resolution as perceived by a viewer. Luminance data may be adjusted in paired frames to improve the perceived color resolution without affecting the perceived luminance values. High edge areas, including high frequency variations in luminance values, may be adaptively desaturated to improve the perceived color resolution of the resulting output.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

In a method to reconstruct a high dynamic range video signal, a decoder receives a base layer standard dynamic range video signal, an enhancement layer video signal, a metadata bitstream for a reference processing unit and a CRC code related to the metadata. A decoder reconstructs a high-dynamic range video output signal based on the base layer video signal, the enhancement layer video signal, and the data syntax and metadata specified by the metadata bitstream.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

Super-resolution of dynamic scenes using sampling rate diversity, processes and systems thereof are provided. The method implementing the processes includes: in a first stage, super-resolving secondary low-resolution (LR) images using a set of primary LR images to create LR dictionaries to represent polyphase components (PPCs) of high resolution (HR) patches of images; and, in a second stage, reverting to a single frame super-resolution (SR) applied to each frame which comprises an entire image, using the HR dictionaries extracted from the super-resolved sequence obtain in the first stage.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into N signal portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

Super-resolution of dynamic scenes using sampling rate diversity, processes and systems thereof are provided. The method implementing the processes includes: in a first stage, super-resolving secondary low-resolution (LR) images using a set of primary LR images to create LR dictionaries to represent polyphase components (PPCs) of high resolution (HR) patches of images; and, in a second stage, reverting to a single frame super-resolution (SR) applied to each frame which comprises an entire image, using the HR dictionaries extracted from the super-resolved sequence obtain in the first stage.