An image processing device 300, a non-transitory computer readable storage medium, a monitoring camera 200 and a method 100 of pre-processing images of a video stream before encoding the video stream are disclosed. The images are obtained S110, wherein the obtained images have a first resolution. The obtained images are subsampled S120 to intermediate images having a second resolution lower than the first resolution and lower than a third resolution. The intermediate images are upsampled S130 to output images having the third resolution, wherein the third resolution is the same for all images of the video stream.

Methods, systems, and devices for luma mapping with chroma scaling for video and image coding are disclosed. An example method of video processing includes performing, for a current region comprising a luma block, a first chroma block, and a second chroma block, a conversion between the current region of a video and a bitstream representation of the video according to a rule that specifies an order in which, during decoding, the first chroma block and the second chroma block are processed based on mapped sample values of the luma block.

Methods, systems, and devices for luma mapping with chroma scaling for video and image coding are disclosed. An example method of video processing includes performing, for a current region comprising a luma block, a first chroma block, and a second chroma block, a conversion between the current region of a video and a bitstream representation of the video according to a rule that specifies an order in which, during decoding, the first chroma block and the second chroma block are processed based on mapped sample values of the luma block.

This disclosure describes systems, methods, and devices related to complexity aware encoding. A device may generate a list of encodes based on pairs of resolution and quantization parameters (QP) pairs associated with one or more video segments received from a source. The device may generate an estimated bit rate associated with the one or more video segments based on an analysis of the one or more video segments. The device may determine distortion values associated with the one or more video segments. The device may apply a weighting mechanism to the distortion values using the estimated bit rate. The device may select a subset of encodes based on the weighting mechanism. The device may perform the subset of encodes on the one or more video segments for transmission.

This disclosure describes systems, methods, and devices related to complexity aware encoding. A device may generate a list of encodes based on pairs of resolution and quantization parameters (QP) pairs associated with one or more video segments received from a source. The device may generate an estimated bit rate associated with the one or more video segments based on an analysis of the one or more video segments. The device may determine distortion values associated with the one or more video segments. The device may apply a weighting mechanism to the distortion values using the estimated bit rate. The device may select a subset of encodes based on the weighting mechanism. The device may perform the subset of encodes on the one or more video segments for transmission.

In non-limiting examples of the present disclosure, systems, methods and devices for determining image compression optimums are provided. An image may be processed with a machine learning model that has been trained to identify object types in digital images. A first object and a first object type of the first object may be identified in the image. A first compressed version of the image may be generated, wherein the first compressed version has a first storage size. The first object and the first object type of the first object may be identified in the first compressed version of the image. A second compressed version of the image may be generated based on the identification of the first object and the first object type in the first compressed version of the image. The second compressed version may have a smaller storage size than the first storage size.

There is disclosed an encoder, a decoder, related methods, and non-transitory storage units storing instructions which, when executed by a computer, cause the computer to perform the methods.

At an encoder (300), after a spatial transformation stage (304), there is obtained a spatially transformed version (306) of input image information (302) having multiple bands and, for each band, multiple transform band coefficients. After the generation of precincts (311), each comprising transform coefficients covering a predetermined spatial area of the input image information (302), there is provided a component transformation stage (320, 325), to apply one component transformation (CTr) selected (327) out of a plurality of predetermined component transformations, to each band (102′) of each precinct (311). Hence, there is obtained a spatially transformed and color transformed version (323) of the input image information (302), which is subsequently quantized and entropy encoded.

There is disclosed an encoder, a decoder, related methods, and non-transitory storage units storing instructions which, when executed by a computer, cause the computer to perform the methods.

At an encoder (300), after a spatial transformation stage (304), there is obtained a spatially transformed version (306) of input image information (302) having multiple bands and, for each band, multiple transform band coefficients. After the generation of precincts (311), each comprising transform coefficients covering a predetermined spatial area of the input image information (302), there is provided a component transformation stage (320, 325), to apply one component transformation (CTr) selected (327) out of a plurality of predetermined component transformations, to each band (102′) of each precinct (311). Hence, there is obtained a spatially transformed and color transformed version (323) of the input image information (302), which is subsequently quantized and entropy encoded.

An apparatus for decoding a sequence of pictures from a data stream is configured for decoding a picture of the sequence by: deriving a residual transform signal of the picture from the data stream; combining the residual transform signal with a buffered transform signal of a previous picture of the sequence so as to obtain a transform signal of the picture, the transform signal representing the picture in spectral components; and subjecting the transform signal to a spectral-to-spatial transformation, wherein the buffered transform signal comprises a selection out of spectral components representing the previous picture.

An apparatus for decoding a sequence of pictures from a data stream is configured for decoding a picture of the sequence by: deriving a residual transform signal of the picture from the data stream; combining the residual transform signal with a buffered transform signal of a previous picture of the sequence so as to obtain a transform signal of the picture, the transform signal representing the picture in spectral components; and subjecting the transform signal to a spectral-to-spatial transformation, wherein the buffered transform signal comprises a selection out of spectral components representing the previous picture.