Hong Kong Applied Science and Technology Research Institute Company, Limited

Hailiang Li

Yan Huo

Tao Li

Yang Liu

Zhi Bin Lei

Man Yau Chiu

Yan Wang

Kangheng Wu

Hing-Mo Lam

Ka Lung Tim Wong

Ho Yu Wong

A lossless data compressor prevents normalization overruns on-the-fly as symbol occurrence counts are rounded to generate symbol frequencies, allowing an encoding table generator to generate encoding table entries without waiting for the symbol frequency table to finish filling. Rounding errors are accumulated as symbols are normalized and compensated for by reducing a symbol frequency when the symbol frequency is at least 2 and the accumulated errors have exceeded a threshold. The symbol frequency is also reduced when the number of remaining states in the encoding table is insufficient for a number of remaining unprocessed symbols and states for a current encoding table entry. Since error compensation occurs as symbols are being normalized, encoding table generation is not forced to wait for all symbols in the block to be processed, reducing latency. Three pipeline stages can operate on three input blocks: symbol counting, normalization/error compensation/encoding table generation, and data encoding.

Hardware implementation of frequency table generation for Asymmetric-Numeral-System-based data compression

A data-compression analyzer can rapidly make a binary decision to compress or not compress an input data block or can use a slower neural network to predict the block's compression ratio with a regression model. A Concentration Value (CV) that is the sum of the squares of the frequencies and a Number of Zero (NZ) symbols are calculated from an un-sorted symbol frequency table. A rapid decision to compress is signaled when their product CV*NZ exceeds a horizontal threshold THH. During training, CV*NZ is plotted as a function of compression ratio C % for many training data blocks. Different test values of THH are applied to the plot to determine true and false positive rates that are plotted as a Receiver Operating Characteristic (ROC) curve. The point on the ROC curve having the largest Youden index is selected as the optimum THH for use in future binary decisions.

Predicting compression ratio of data with compressible decision

A train-linking lossless data compressor examines a block of data and uses a same coder to generate a same code when all data values in the input block are identical. When the input data is not all the same value, then a Gaussian coder, a Laplace coder, and a delta coder are activated in parallel. The three compressed code lengths are compared and the smallest code length is output as the compressed code when it is smaller than a copy code length. The copy code is a tag followed by copying all the data in the input block. When the smallest of the three compressed code lengths is larger than the copy code length, the file is not compressible, and the copy code is output. No frequency table is required so latency is low. The delta coder subtracts data values from an average value of the last data block.

Train-linking lossless compressor of numeric values

Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described. Embodiments apply a trained random forest classifier to low-resolution patches generated from the low-resolution input image to classify the low-resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier 'votes' for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

Systems and methods for super-resolution synthesis based on weighted results from a random forest classifier

A method for a vehicle management system with a perspective view camera generating perspective images of vehicles in a traffic direction, including performing an anti-perspective transform on the perspective images; separating each anti-perspective image into sub-images along one of a radial direction and a tangential direction relative to the traffic direction; determining a scale factor for each sub-image based upon measuring a scale of each vehicle at plural positions in one image using a frame difference method performing a scale transform for each sub-image using the corresponding scale factors for the sub-image; combining each of the scale transformed sub-images for each vehicle into corresponding enhanced anti-perspective images; performing vehicle detection for each vehicle based a combination of the enhanced anti-perspective images in which tracking is enhanced with an optimized detection box size range determined by the enhanced anti-perspective images; and performing vehicle tracking for each vehicle based on the combination of the enhanced anti-perspective images in which detecting is enhanced with the optimized detection box size range determined by the enhanced anti-perspective images.

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Hailiang Li