Abstract: Techniques are disclosed relating to interpolation for texture mapping. In some embodiments, a graphics unit includes circuitry configured to map a texture to a screen space such that a set of multiple in the screen space falls between first and second adjacent texels of the texture in a first dimension. In some embodiments, the graphics unit also includes texture processing circuitry configured to perform different types of interpolation for pixels in the group of pixels. In these embodiments, this includes determining pixel attributes for first and second end groups of pixels in the set of pixels using a nearest-neighbor interpolation technique and attributes of the first and second texels respectively. In these embodiments, this also includes determining pixel attributes for an intermediate group of pixels in the set of pixels using a second, different interpolation technique and attributes of both the first and second texels.

Abstract: Techniques are disclosed relating to interpolation for texture mapping. In some embodiments, a graphics unit includes circuitry configured to map a texture to a screen space such that a set of multiple in the screen space falls between first and second adjacent texels of the texture in a first dimension. In some embodiments, the graphics unit also includes texture processing circuitry configured to perform different types of interpolation for pixels in the group of pixels. In these embodiments, this includes determining pixel attributes for first and second end groups of pixels in the set of pixels using a nearest-neighbor interpolation technique and attributes of the first and second texels respectively. In these embodiments, this also includes determining pixel attributes for an intermediate group of pixels in the set of pixels using a second, different interpolation technique and attributes of both the first and second texels.

Abstract: A filtering method and apparatus for anti-aliasing takes advantage of improved existing hardware by using as input the data stored in the multisampling anti-aliasing (MSAA) buffers after rendering. The standard hardware box-filter is then replaced with a more intelligent resolve implemented using shader programs. Embodiments find scene edges using existing samples generated by Graphics Processing Unit (GPU) hardware. Using samples from a footprint larger than a single pixel, a gradient is calculated matching the direction of an edge. A non-linear filter over contributing samples in the direction of the gradient gives the final result.

Abstract: Embodiments of a filtering method and apparatus for anti-aliasing as described herein take advantage of improved existing hardware by using as input the data stored in the multisampling anti-aliasing (MSAA) buffers after rendering. The standard hardware box-filter is then replaced with a more intelligent resolve implemented using shader programs. Embodiments find scene edges using existing samples generated by Graphics Processing Unit (GPU) hardware. Using samples from a footprint larger than a single pixel, a gradient is calculated matching the direction of an edge. A non-linear filter over contributing samples in the direction of the gradient gives the final result.

Abstract: A processing unit, method, and graphics processing system are provided for processing a plurality of frames of graphics data. For instance, the processing unit can include a first plurality of graphics processing units (GPUs), a second plurality of GPUs, and a plurality of compositors. The first plurality of GPUs can be configured to process a first frame of graphics data. Likewise, the second plurality of GPUs can be configured to process a second frame of graphics data. Further, each compositor in the plurality of compositors can be coupled to a respective GPU from the first and second pluralities of GPUs, where the plurality of compositors is configured to sequentially pass the first and second frames of graphics data to a display module.

Abstract: The present invention provides an analysis method that uses error metrics to determine whether an image is suitable for compression. One embodiment of the present invention can make the determination in real-time. In one embodiment, analysis methods based on four error metrics are used to determine whether a compressed image should be used. The first metric is a signal-to-noise (SNR) error metric that prevents compressed images with low SNR from being used to represent original images. Another metric is detecting the geometric correlation of pixels within individual blocks of the compressed images. The third metric is used to determine whether color mapping in the compressed images are well-mapped. A final metric is a size metric that filters images smaller than a certain size and prevents them from being compressed. As most analysis methods are integral parts of the compression process, the present invention incurs little cost collecting error metric data.

Abstract: A processing unit, method, and graphics processing system are provided for processing a plurality of frames of graphics data. For instance, the processing unit can include a first plurality of graphics processing units (GPUs), a second plurality of GPUs, and a plurality of compositors. The first plurality of GPUs can be configured to process a first frame of graphics data. Likewise, the second plurality of GPUs can be configured to process a second frame of graphics data. Further, each compositor in the plurality of compositors can be coupled to a respective GPU from the first and second pluralities of GPUs, where the plurality of compositors is configured to sequentially pass the first and second frames of graphics data to a display module.

Abstract: Embodiments of a filtering method and apparatus for anti-aliasing as described herein take advantage of improved existing hardware by using as input the data stored in the multisampling anti-aliasing (MSAA) buffers after rendering. The standard hardware box-filter is then replaced with a more intelligent resolve implemented using shader programs. Embodiments find scene edges using existing samples generated by Graphics Processing Unit (GPU) hardware. Using samples from a footprint larger than a single pixel, a gradient is calculated matching the direction of an edge. A non-linear filter over contributing samples in the direction of the gradient gives the final result.

Abstract: A system and method for improved antialiasing in video processing is described herein. Embodiments include multiple video processors (VPUs) in a system. Each VPU performs some combination of pixel sampling and pixel center sampling (also referred to as multisampling and supersampling). Each VPU performs sampling on the same pixels or pixel centers, but each VPU creates sample positioned differently from the other VPUs corresponding samples. The VPUs each output frame data that has been multisampled and/or supersampled into a compositor that composites the frame data to produce an antialiased rendered frame. The antialiased rendered frame has an effectively doubled antialiasing factor.

Abstract: The present invention provides an analysis method that uses error metrics to determine whether an image is suitable for compression. One embodiment of the present invention can make the determination in real-time. In one embodiment, analysis methods based on four error metrics are used to determine whether a compressed image should be used. The first metric is a signal-to-noise (SNR) error metric that prevents compressed images with low SNR from being used to represent original images. Another metric is detecting the geometric correlation of pixels within individual blocks of the compressed images. The third metric is used to determine whether color mapping in the compressed images are well-mapped. A final metric is a size metric that filters images smaller than a certain size and prevents them from being compressed. As most analysis methods are integral parts of the compression process, the present invention incurs little cost collecting error metric data.