Abstract: A method for partitioning a block of an image to reduce quantization artifacts includes estimating an expected entropy of the block; partitioning the block into sub-blocks, where each sub-block having a size of a smallest possible partition size; calculating respective amounts of visual masking for the sub-blocks; selecting, as a visual masking characteristic of the block, a highest visual masking value of the respective amounts of visual masking for the sub-blocks; combining the visual masking characteristic of the block and the expected entropy of the block to obtain a splitting indicator value; and determining whether to split the block based on the splitting indicator.

Abstract: Methods are provided for improving the quality and compression factor of compressed images. The methods include determining frequency-band-specific quantization levels on a block-by-block level. This results in an adaptive dead zone, allowing certain blocks to be represented by fewer nonzero elements while other blocks are represented by more nonzero elements. Accordingly, the quality of the encoded image is improved while maintaining or improving the compression ratio. The adaptive quantization level is determined by comparing a post-quantization energy level to a threshold energy criterion for each frequency band within a block. Where the energy threshold criterion is not satisfied via these methods, additional methods can be applied to improve the image quality.

Abstract: Implementations disclosed describe techniques used for compiling a quantum algorithm for execution on a plurality of quantum circuits, including accessing, by a processing device, the quantum algorithm, identifying a matrix associated with the quantum algorithm, determining a representation of the identified matrix as a matrix decomposition that includes a plurality of transformation matrices, wherein one or more of the plurality of transformation matrices perform multiple instances of two-dimensional rotations; and generating a circuit map that maps execution of the matrix decomposition on the plurality of quantum circuits.

Abstract: Example embodiments relate to using a multi-context entropy coder for encoding adjacency lists. A system may obtain a graph having data (or multiple graphs) and may compress the data of the graph using a multi -context entropy coder. The multi-context entropy coder may encode adjacency lists within the data such that each integer is assigned to a different probability distribution. For example, operating the multi-context entropy coder may involve using a combination of arithmetic coding, Huffman coding, and ANS. The assignment of integers to the probability distributions may depend on each integer’s role and/or previous values of a similar kind. By using multi -context entropy- coding, the computing system may increase compression ratio while maintaining similar processing speed.

Abstract: A method can include compressing a first original frame of a video stream to an intraframe, the intraframe comprising fewer symbols than the first original frame, compressing a second original frame to a first interframe, the first interframe referencing the intraframe and comprising fewer symbols than the second original frame, determining an intraframe error of the intraframe due to the compression of the first original frame, determining a first interframe error of the first interframe due to the compression of the second original frame, determining a compression level for a third original frame based on the intraframe error and the first interframe error, and compressing the third original frame to a second interframe, the second interframe referencing the intraframe and the first interframe and comprising fewer symbols than the third original frame, a number of symbols included in the second interframe being based on the determined compression level.

Abstract: The present disclosure proposes a model that has more expressive power, e.g., can generalize from a smaller amount of parameters and assign more computation in areas of the function that need more computation. In particular, the present disclosure is directed to novel machine learning architectures that use the exponential of an input-dependent matrix as a nonlinearity. The mathematical simplicity of this architecture allows a detailed analysis of its behavior.

Abstract: The present disclosure proposes a model that has more expressive power, e.g., can generalize from a smaller amount of parameters and assign more computation in areas of the function that need more computation. In particular, the present disclosure is directed to novel machine learning architectures that use the exponential of an input-dependent matrix as a nonlinearity. The mathematical simplicity of this architecture allows a detailed analysis of its behavior, providing stringent robustness guarantees via Lipschitz bounds.

Abstract: A method can include compressing a first original frame of a video stream to an intraframe, the intraframe comprising fewer symbols than the first original frame, compressing a second original frame to a first interframe, the first interframe referencing the intraframe and comprising fewer symbols than the second original frame, determining an intraframe error of the intraframe due to the compression of the first original frame, determining a first interframe error of the first interframe due to the compression of the second original frame, determining a compression level for a third original frame based on the intraframe error and the first interframe error, and compressing the third original frame to a second interframe, the second interframe referencing the intraframe and the first interframe and comprising fewer symbols than the third original frame, a number of symbols included in the second interframe being based on the determined compression level.