Abstract: The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.

Abstract: An context-based encoding mechanism uses a predetermined number of bytes in a previous segment of a file to determine a context for the current segment. The current segment is encoded using a dictionary that corresponds to the determined context. An example method includes determining, for a first segment in a data file, a first context state based on a first context segment within the data file that precedes the first segment, identifying a first indexed dictionary from a plurality of indexed dictionaries based on the first context state, and encoding the first segment using the identified first indexed dictionary.

Abstract: The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.

Abstract: The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.

Abstract: An image block is coded using entropy-inspired directional filtering. During encoding, intensity differences are determined for at least some pixels of an image block based on neighboring pixels of respective ones of the at least some pixels. Angles are estimated for each of those pixels based on the intensity differences. A main filtering direction of the image block is then determined based on the estimated angles. The image block is filtered according to the main filtering direction to remove artifacts along oblique edges associated with the image block. The filtered image block is then encoded to an encoded image. During decoding, an angular map indicating angles estimated for pixels of an encoded image block is received and used to determine the main filtering direction of the image block. The image block can then be filtered according to the main filtering direction and then output for display or storage.

Abstract: A method for encoding a block of a frame. The method includes receiving the block that is in the spatial domain; selecting, based on a border that crosses the block, a set of basis functions for transforming the block to a transform domain; transforming the block using the set of basis functions to obtain a transform block; encoding, in a compressed bitstream, an indication of the set of basis functions; and encoding, in the compressed bitstream, the transform block.

Abstract: The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.

Abstract: A method for encoding a block of a frame. The method includes receiving the block that is in the spatial domain; selecting, based on a border that crosses the block, a set of basis functions for transforming the block to a transform domain; transforming the block using the set of basis functions to obtain a transform block; encoding, in a compressed bitstream, an indication of the set of basis functions; and encoding, in the compressed bitstream, the transform block.

Abstract: An image block is coded using entropy-inspired directional filtering. During encoding, intensity differences are determined for at least some pixels of an image block based on neighboring pixels of respective ones of the at least some pixels. Angles are estimated for each of those pixels based on the intensity differences. A main filtering direction of the image block is then determined based on the estimated angles. The image block is filtered according to the main filtering direction to remove artifacts along oblique edges associated with the image block. The filtered image block is then encoded to an encoded image. During decoding, an angular map indicating angles estimated for pixels of an encoded image block is received and used to determine the main filtering direction of the image block. The image block can then be filtered according to the main filtering direction and then output for display or storage.

Abstract: An image block is coded using entropy-inspired directional filtering. During encoding, intensity differences are determined for at least some pixels of an image block based on neighboring pixels of respective ones of the at least some pixels. Angles are estimated for each of those pixels based on the intensity differences. A main filtering direction of the image block is then determined based on the estimated angles. The image block is filtered according to the main filtering direction to remove artifacts along oblique edges associated with the image block. The filtered image block is then encoded to an encoded image. During decoding, an angular map indicating angles estimated for pixels of an encoded image block is received and used to determine the main filtering direction of the image block. The image block can then be filtered according to the main filtering direction and then output for display or storage.

Abstract: An context-based encoding mechanism uses a predetermined number of bytes in a previous segment of a file to determine a context for the current segment. The current segment is encoded using a dictionary that corresponds to the determined context. An example method includes determining, for a first segment in a data file, a first context state based on a first context segment within the data file that precedes the first segment, identifying a first indexed dictionary from a plurality of indexed dictionaries based on the first context state, and encoding the first segment using the identified first indexed dictionary.

Abstract: A method includes obtaining, at one or more computing devices, an input image, applying an image sharpening process to at least one of one or more chroma components of the input image, subsampling the one or more chroma components of the input image to reduce a spatial resolution of the one or more chroma components of the input image, encoding the input image subsequent to applying the image sharpening process and subsampling, and performing at least one of storing or transmitting the input image subsequent to encoding.

Abstract: Computer-implemented systems and methods are provided for extracting and storing information regarding entities from documents, such as webpages. In one implementation, a system is provided that detects an entity candidate in a document and determines that the detected candidate is a new entity. The system also detects a known entity proximate to the known entity based on the one or more entity models. The system also detects a context proximate to the new and known entities having a lexical relationship to the known entity. The system also determines a second entity class associated with the known entity and a context class associated with the context. The system also generates a first entity class based on the second entity class and the context class. The system also generates an entry in the one or more entity models reflecting an association between the new entity and the first entity class.

Abstract: Computer-implemented systems and methods are disclosed for building knowledge bases, such as knowledge graphs, using context clouds. According to certain embodiments, a target object is identified in a portion of unstructured or semi-structured data in a target document, which does not conform to a predefined structure or pattern. A knowledge server may build a context cloud for the target document. The knowledge server may analyze one or more other documents stored in a networked database, to identify candidate documents that may include a meaning or relationship associated with the target object. The knowledge server may analyze one or more context clouds for the candidate documents to determine a meaning or relationship of the target object based on objects in the candidate document(s). The knowledge server may associate the determined meanings and/or relationships with the target object in the target document, thereby creating a new portion of a knowledge graph.

Abstract: Systems and methods for optimizing the compression of data using a dictionary coding compression system are described. A system performs an initial compression operation, such as by using a dictionary or other coder. The system then refines the compression by performing an iterative backward reference search for an optimized compressed file.