Google Inc.

Zoltan Szabadka

Jyrki Antero Alakuijala

Jan Wassenberg

Robert Obryk

Manjunath Srinivasaiah

Lode Vandevenne

Jyrki Alakuijala

Minttu Alakuijala

Evgenii Kliuchnikov

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

Video decoding may include transform coefficient continuity smoothing, which may include coefficient continuity smoothing, defined correlation coefficient smoothing, pixel range projection, and luminance correlated chrominance resampling. Coefficient continuity smoothing may include obtaining encoded block data from the encoded bitstream, the encoded block data corresponding to a current block from the reconstructed frame, and generating reconstructed block data for the current block based on the encoded block data using transform coefficient continuity smoothing. Transform coefficient continuity smoothing may include, for a block boundary of the current block, in response to a determination that adjacent block data corresponding to an adjacent block spatially adjacent to the current block along the block boundary is available, performing transform coefficient continuity smoothing based on the current block, the adjacent block, and the block boundary, and including the reconstructed block data in the reconstructed frame, and outputting the reconstructed frame.

Block artefact reduction

Techniques of compressing color images in the presence of chromatic aberrations involve performing, prior to compression, a chromatic aberration correction operation on a color image. Along these lines, the chromatic aberration of an imaging system may be represented as a vector displacement map between a red channel and a green channel of a color image, a blue channel and a green channel of the color image, or both. In some implementations, prior to adding the vector displacements to each of the images of the red channel and the blue channel, these displacements are weighted according to proximity from an edge of each of the respective red and blue images. In some further implementations, the vector displacement maps as well as the weights are blurred with a blurring kernel such as a gaussian. Once these vector displacements are added to each of the red and blue images, the resulting color images are linearly combined to produce a new brightness channel Y. This new brightness channel Y is compressed to form the compressed color image.

Chromatic aberration modeling in image compression and enhancement

An improved color space (YHB model) for compressing image files is provided. An example method includes storing a sum of an unweighted first color value and an unweighted second color value for each pixel in a plurality of pixels of an image as a first channel, sub sampling, among the plurality of pixels, a difference between the first color value and the second color value as a second channel, sub sampling, among the plurality of pixels, a third color value as a third channel, and storing the first channel, the second channel, and the third channel as the compressed image. In some implementations, the original image may be split into a low frequency version and a high frequency version. The system may apply the YHB model to the high frequency version and apply a conventional model or a second variation of the YHB model to the low frequency version.

Color space compression

Morphological anti-ringing is disclosed. A method for encoding a block of an image includes generating a transform block for the block of first values, generating quantized transform coefficients for the transform block, determining a clamping value for the block, encoding the quantized transform coefficients in a compressed bitstream, and encoding the clamping value in the compressed bitstream. The clamping value is used to clamp second values. The second values correspond to the first values and result from inverse transforming the transform block. An apparatus for decoding a block of an image includes a memory and a processor configured to execute instructions stored in the memory to decode, from a compressed bitstream, a quantized transform block; obtain, using the quantized transform block, a decoded block of pixel values; decode, from a compressed bitstream, a clamping value for the block; and clamp the pixel values using the clamping value.

Morphological anti-ringing filter for lossy image compression

Methods, systems, apparatus, including computer-readable media storing executable instructions, for color filter arrays for image sensors. In some implementations, an imaging device includes a color filter array arranged to filter incident light. The color filter array has a repeating pattern of color filter elements. The color filter elements include yellow filter elements, green filter elements, and blue filter elements. The imaging device includes an image sensor having photosensitive regions corresponding to the color filter elements. The photosensitive regions are configured to respectively generate electrical signals indicative of intensity of the color-filtered light at the photosensitive regions. The imaging device includes one or more processors configured to generate color image data based on the electrical signals from the photosensitive regions.

Color filter arrays for image sensors

A non-transitory computer-readable storage medium may include instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to compress data by storing positions of strings that hash to a same hash value in a ring buffer, and retrieving the ring buffer in a single memory operation to determine a longest matching string that hashes to the same hash value.

Addressing recent strings with ring buffer

An improved color space (YHB model) for compressing image files is provided. An example method includes storing a sum of an unweighted first color value and an unweighted second color value for each pixel in a plurality of pixels of an image as a first channel, subsampling, among the plurality of pixels, a difference between the first color value and the second color value as a second channel, subsampling, among the plurality of pixels, a third color value as a third channel, and storing the first channel, the second channel, and the third channel as the compressed image. In some implementations, the original image may be split into a low frequency version and a high frequency version. The system may apply the YHB model to the high frequency version and apply a conventional model or a second variation of the YHB model to the low frequency version.

Data may be decompressed by receiving a compressed sequence of characters, the compressed sequence of characters being represented by at least a first received number, dividing the first received number by a number of words in a corpus of words to determine a quotient and a remainder, retrieving a word from the corpus of words based on the remainder, retrieving a transformation from a transformation index based on the quotient, and performing the retrieved transformation on the retrieved word. The representations of characters included in the transformed word may be a decompressed version of the received compressed sequence of characters.

Encoding or decoding characters as word in corpus

A non-transitory computer-readable storage medium comprising instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to at least determine, for each of a plurality of current symbols in a data block, frequencies of succeeding symbols within the data block, generate a plurality of clusters, each of the plurality of clusters including a subset of the plurality of current symbols, generate, for each of the clusters, a code, the code including variable length codewords for each of the succeeding symbols of the current symbols included in the respective cluster, and encode each of the succeeding symbols in the data block based on the code for the cluster that includes the succeeding symbol's respective current symbol.

Encoding and decoding data using context model grouping

Embodiments disclosed herein generally disclose how to provide geocoding results in response to location search queries. A geographical information system receives a search query for a map feature and identifies both web search results and geocoding results that are associated with the search query. The geographical information system determines which geocoding result to return based on the identified web search results.

Growing community of inventors

Wollerau, Switzerland

Zoltan Szabadka