Google Inc.

Jan Wassenberg

Jyrki Antero Alakuijala

Robert Obryk

Emmanuel Mogenet

Sami Boukortt

Zoltan Szabadka

Lode Vandevenne

Alkis Evlogimenos

Tomas Dzetkulic

Evgenii Kliuchnikov

Minttu Alakuijala

Marcin Kowalczyk

A method for generating random numbers includes initializing a pseudo-random number generator (PRNG) having a state of 2048 bits comprising inner bits and outer bits, the inner bits comprising the first 128 bits of the 2048 bits and the outer bits comprising the remaining bits of the 2048 bits. The method also includes retrieving AES round keys from a key source, and for a threshold number of times, executing a round function using the AES round keys by XOR'ing odd-numbered branches of a Feistel network having 16 branches of 128 bits with a function of corresponding even-numbered neighbor branches of the Feistel network, and shuffling each branch of 128 bits into a prescribed order. The method also includes executing an XOR of the inner bits of the permuted state with the inner bits of a previous state.

Random number generator

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.

Image compression and decompression using controlled quality loss

A decoder may perform a method of decompressing images that include texture features that are not aligned with an axis of the image being compressed. In some example implementation, the method may include receiving a block of geometrically transformed pixel values and performing an inverse geometric transformation on the block of geometrically transformed pixel values to generate a first block of pixel values. The geometrically transformed pixel values represent texture features of an image that are non-parallel with a vertical axis or a horizontal axis of the image and the first block of pixel values being one of a plurality of blocks of the image. The example method may further include generating at least a portion of the image based on the first block of pixel values.

Geometric transforms for image compression

A method includes obtaining respective filtered pixels for pixels of a reconstructed image; and obtaining an edge-preserved image using the respective filtered pixels. Obtaining the respective filtered pixels includes, for each pixel of the reconstructed image, obtaining a respective filtered pixel by selecting a pixel patch including the pixel and first neighboring pixels of the pixel that are at relative neighboring locations with respect to the pixel; calculating respective weights for the first neighboring pixels; and filtering the pixel using the respective weights of the first neighboring pixels and the neighboring pixels to obtain the respective filtered pixel. Calculating the respective weights includes, for each neighboring pixel of the first neighboring pixels, forming a neighboring patch including the neighboring pixel and second neighboring pixels, and calculating a neighboring patch distance between the pixel patch and the neighboring pixel; and calculating a respective weight using the neighboring patch distance.

Iterative IDCT with adaptive non-linear filtering

Systems and methods generate reasonably secure hash values at relatively few CPU cycles per byte. An example method includes, for each of a plurality of packets, injecting the packet into an internal state that represents an internal hash sum, mixing the internal state using multiplication, and shuffling the result of the multiplication so that bytes with highest quality are moved to locations that will propagate most widely in a next multiplication operation. Each of the plurality of packets include data from an input to be hashed. In some implementation, a last packet for the input is padded. The method may also include further mixing the internal state using multiplication after processing the plurality of packets and providing, to a requesting process, a portion of the final internal state as a hash of the input.

Hashing using data parallel instructions

A spatially adaptive quantization-aware deblocking filter is used for encoding or decoding video or image frames. The deblocking filter receives a reconstructed frame produced based on dequantized and inverse transformed coefficients of a video frame or an image frame. The reconstructed frame is filtered according to adaptive quantization field data for the video or image frame. The adaptive quantization field data represents weights applied to quantization values used at different areas of the video or image frame. A number of blocking artifacts remaining within the resulting filtered frame is determined. The adaptive quantization field data is then adjusted based on that number of blocking artifacts. The filtered frame is then filtered according to the adjusted adaptive quantization field data. The resulting re-filtered frame is then output to an output source, such as for transmission, display, storage, or further processing.

Spatially adaptive quantization-aware deblocking filter

A fast cryptographic hash of an input file using multiplication and permutation operations in a parallel processing environment. An example method includes updating an internal state for each of a plurality of packets, the packets being read from an input file. Updating the state for a packet can include injecting the packet into an internal state, mixing the bits of the internal state using multiplication, and shuffling the result of the multiplication so that bits with highest quality are permuted to locations that will propagate most widely in a next multiplication operation. The method also includes performing a reduction on the internal state and repeating the update of the internal state, the reduction, and the injecting a second time. The method may further include finalizing the internal state and storing a portion of the final internal state as a cryptographic hash of the input file.

Cryptographic hash generated using data parallel instructions

An encoder and/or a computer implemented encoding method includes a texture module configured to determine texture data associated with texture of an image, a noise module configured to determine noise data based on the texture data, a synthesis module configured to generate spatial spectral characteristics of the noise, and combine at least one of the noise data, the texture data, and the spatial spectral characteristics of the noise based on at least one border between adjacent textures, and an encoding module configured to compress the image using an image compression codec.

Mixed noise and fine texture synthesis in lossy image compression

A method for decoding an image block includes receiving a quantized transform block; generating a decoded block from the quantized transform block; applying an edge-preserving filter to the decoded block, to obtain an edge-filtered decoded block; obtaining a transformed edge-preserved block using a transform type and quantization data to the edge-filtered decoded block; clamping a value of the transformed edge-preserved block to a corresponding value of the quantized transform block to obtain a smoothed transform block; and inverse transforming the smoothed transform block to obtain an edge-preserved smoothed block. The applying an edge-preserving filter to the decoded block includes determining respective patch-based distances between a pixel of the decoded block and neighboring pixels; determining respective weights corresponding to the respective patch-based distances; and filtering the pixel using the respective weights and the neighboring pixels.

This technology relates to encoding data. For example, a sequence of one or more structured records as input data, at least one of the structured records including one or more field tags and associated field data. The input data may be parsed into data buffers, each data buffer corresponding to a field tag in the one or more field tags, wherein each data buffer includes the associated field data of the corresponding field tag. A control sequence specifying a sequence of the one or more fields tags may be encoded into a transition record. A state machine comprising nodes and transitions may be generated, with each node corresponding to occurrences of the one or more field tags and each transition corresponding to successive pairs of the one or more field tags. The data buffers, a representation of the state machine, and the encoded control sequence may be output.

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Langnau am Albis, Switzerland

Jan Wassenberg