A method includes receiving transform coefficients corresponding to a scaled video input signal, the scaled video input signal including a plurality of spatial layers that include a base layer. The method also includes determining a spatial rate factor based on a sample of frames from the scaled video input signal. The spatial rate factor defines a factor for bit rate allocation at each spatial layer of an encoded bit stream formed from the scaled video input signal. The spatial rate factor is represented by a difference between a rate of bits per transform coefficient of the base layer and an average rate of bits per transform coefficient. The method also includes reducing a distortion for the plurality of spatial layers by allocating a bit rate to each spatial layer based on the spatial rate factor and the sample of frames.

Provided is a method of constructing a neural network translation model. The method includes generating a first neural network translation model learning a feature of source domain data used in an unspecific field, generating a second neural network translation model learning a feature of target domain data used in a specific field, generating a third neural network translation model learning a common feature of the source domain data and the target domain data; and generating a combiner combining translation results of the first to third neural network translation models.

The precision of up-sampling operations in a layered coding system is preserved when operating on video data with high bit-depth. In response to bit-depth requirements of the video coding or decoding system, scaling and rounding parameters are determined for a separable up-scaling filter. Input data are first filtered across a first spatial direction using a first rounding parameter to generate first up-sampled data. First intermediate data are generated by scaling the first up-sampled data using a first shift parameter. The intermediate data are then filtered across a second spatial direction using a second rounding parameter to generate second up-sampled data. Second intermediate data are generated by scaling the second up-sampled data using a second shift parameter. Final up-sampled data may be generated by clipping the second intermediate data.

The precision of up-sampling operations in a layered coding system is preserved when operating on video data with high bit-depth. In response to bit-depth requirements of the video coding or decoding system, scaling and rounding parameters are determined for a separable up-scaling filter. Input data are first filtered across a first spatial direction using a first rounding parameter to generate first up-sampled data. First intermediate data are generated by scaling the first up-sampled data using a first shift parameter. The intermediate data are then filtered across a second spatial direction using a second rounding parameter to generate second up-sampled data. Second intermediate data are generated by scaling the second up-sampled data using a second shift parameter. Final up-sampled data may be generated by clipping the second intermediate data.

A method, for generating (a) a forward reshaping function for compressing an input high-dynamic range (HDR) image into a reshaped standard-dynamic-range (SDR) image and (b) a backward reshaping function for decompressing the reshaped SDR image into a reconstructed HDR image, includes (i) optimizing the forward reshaping function to minimize a deviation between the reshaped SDR image and an input SDR image corresponding to the input HDR image, (ii) optimizing the backward reshaping function to minimize a deviation between the reconstructed HDR image and the input HDR image, and (iii) until a termination condition is met, applying a correction to the input SDR image and reiterating, based on the input SDR image as corrected, the steps of optimizing the forward and backward reshaping functions.

A method for viewing pathology images in a web browser is provided. The method includes obtaining a pathology image in a first format, converting the pathology image into a pyramid representation file comprising images grouped into a plurality of levels, wherein the images in the different plurality of levels correspond to portions of the pathology image at a same or different degrees of resolution, and wherein the images are in the first format, storing the pyramid representation file in the first memory, receiving a request from a user to view the pathology image at a specified resolution, loading one or more images from at least one of the plurality of levels corresponding to the specified resolution, wherein the one or more images are in the first format and wherein the one or more images are loaded into a web browser coupled with the first memory, converting the images into a second format such that the images' degrees of resolution are maintained, and storing the images in the second format in a second memory.

Several systems and methods for intra-prediction estimation of video pictures are disclosed. In an embodiment, the method includes accessing four ‘N×N’ pixel blocks comprising luma-related pixels. The four ‘N×N’ pixel blocks collectively configure a ‘2N×2N’ pixel block. A first pre-determined number of candidate luma intra-prediction modes is accessed for each of the four ‘N×N’ pixel blocks. A presence of one or more luma intra-prediction modes that are common among the candidate luma intra-prediction modes of at least two of the four ‘N×N’ pixel blocks is identified. The method further includes performing, based on the identification, one of (1) selecting a principal luma intra-prediction mode for the ‘2N×2N’ pixel block and (2) limiting a partitioning size to a ‘N×N’ pixel block size for a portion of the video picture corresponding to the ‘2N×2N’ pixel block.

Several systems and methods for intra-prediction estimation of video pictures are disclosed. In an embodiment, the method includes accessing four ‘N×N’ pixel blocks comprising luma-related pixels. The four ‘N×N’ pixel blocks collectively configure a ‘2N×2N’ pixel block. A first pre-determined number of candidate luma intra-prediction modes is accessed for each of the four ‘N×N’ pixel blocks. A presence of one or more luma intra-prediction modes that are common among the candidate luma intra-prediction modes of at least two of the four ‘N×N’ pixel blocks is identified. The method further includes performing, based on the identification, one of (1) selecting a principal luma intra-prediction mode for the ‘2N×2N’ pixel block and (2) limiting a partitioning size to a ‘N×N’ pixel block size for a portion of the video picture corresponding to the ‘2N×2N’ pixel block.

A video decoder has a plurality of processing circuits, including a first processing circuit and a second processing circuit. The first processing circuit applies a first decoding process to a current coding block according to reconstructed neighbor samples, and has a local neighbor buffer for buffering the reconstructed neighbor samples used by the first decoding process. The second processing circuit applies a second decoding process to the current coding block according to at least a portion of the reconstructed neighbor samples retrieved from the local neighbor buffer, wherein the second decoding process is different from the first decoding process.

A video decoder has a plurality of processing circuits, including a first processing circuit and a second processing circuit. The first processing circuit applies a first decoding process to a current coding block according to reconstructed neighbor samples, and has a local neighbor buffer for buffering the reconstructed neighbor samples used by the first decoding process. The second processing circuit applies a second decoding process to the current coding block according to at least a portion of the reconstructed neighbor samples retrieved from the local neighbor buffer, wherein the second decoding process is different from the first decoding process.