Abstract: Systems, apparatuses, and methods for implementing automatic data format detection techniques are disclosed. A graphics engine receives data of indeterminate format and the graphics engine predicts an organization of the data. As part of the prediction, the graphics engine predicts the pixel depth (i.e., bytes per pixel (BPP)) and format separately. The graphics engine folds the data along pixel and channel boundaries to help in determining the pixel depth and format. The graphics engine scores modes against each other to generate different predictions for different formats. Then, the graphics engine generates scores for the predictions to determine which mode has a highest correlation with the input data. Next, the graphics engine chooses the format which attains the best score among the scores that were generated for the different modes. Then, the graphics engine compresses the unknown data using the chosen format with the best score.

Abstract: Systems, apparatuses, and methods for converting pixel data to a custom swizzle mode are disclosed. A graphics engine receives data in a pre-defined swizzle mode. The graphics engine determines a custom swizzle mode for the data that has directionality aligned to the data itself to further optimize deltas that are used for compressing the data. The graphics engine groups incoming data into group of two neighboring pixels in both the horizontal and vertical directions. The graphics engine scores horizontal and vertical groupings against each other to make a first swizzle mode bit selection. Then the graphics engine increases the grouping of pixels to include additional pixels and scores the increased groupings against each other to make subsequent swizzle mode bit selections. The data is reswizzled into the custom swizzle mode and provided to a compressor to be compressed.

Abstract: Systems, apparatuses, and methods for implementing automatic data format detection techniques are disclosed. A graphics engine receives data of indeterminate format and the graphics engine predicts an organization of the data. As part of the prediction, the graphics engine predicts the pixel depth (i.e., bytes per pixel (BPP)) and format separately. The graphics engine folds the data along pixel and channel boundaries to help in determining the pixel depth and format. The graphics engine scores modes against each other to generate different predictions for different formats. Then, the graphics engine generates scores for the predictions to determine which mode has a highest correlation with the input data. Next, the graphics engine chooses the format which attains the best score among the scores that were generated for the different modes. Then, the graphics engine compresses the unknown data using the chosen format with the best score.