Run-length encoding

About: Run-length encoding is a research topic. Over the lifetime, 504 publications have been published within this topic receiving 4441 citations. The topic is also known as: RLE.

Encoding system for picture signal and its apparatus

Abstract: PURPOSE:To improve the efficiency of encoding by outputting a run length code with the shortest code length among plural run length codes and an identification signal in the scanning order corresponding to the run length code in the unit of blocks. CONSTITUTION:A conversion section 3 reads n-set of scanning tables in order and sends a linear picture signal converted according to each individual scanning table to a run length encoding section 4 sequentially. The run length encoding section 4 uses the length of the consecutive white picture elements and black picture elements with respect to the received linear picture signal from the conversion section 3 as a run length so as to count it and the code corresponding to the run length is generated and the run length code is generated to one- block picture signal and sent to a code storage section 6 and the code length (bit number) of the run length code is sent to the code length storage section 5. The run length encoding section 4 applies processing to each of linear pictures signals (n-set) subject to conversion by using n-set of scanning tables.

Digital image expansion system

Abstract: PROBLEM TO BE SOLVED: To effectively convert images by reducing the amount of data to be prepared in the case of performing expansion to the images of various sizes based on the compression data of the images and shortening the processing time for inverse quantization in the case of generating reduction images. SOLUTION: A decoding part 1 Huffman decodes and run length decodes the compression data of one block and generates quantized data Ruv. The quantized data Ruv form the matrix of 8×8 for instance. Since Huffman encoding and run length encoding are reversible encoding, the decoded quantized data Ruv are completely the same as the quantized data Ruu at the time of performing JPEG(joint photographic expert group) compression. A reduction part 2 omits the high frequency component of the quantized data Ruv and performs conversion to the matrix of a small dimension. Thus, the amount of the data to be prepared is reduced in the case of performing the expansion to the images of the various sizes based on the compression data of the images. Then, in the case of generating the reduction images, the processing time for the inverse quantization is shortened and the reduction images faithful to source images are obtained.

Software development kit for lidar data

Abstract: The present invention relates to a method and system for compressing and retrieving Light Detection and Ranging output data, and, more specifically, to a method and system for compressing Light Detection and Ranging output data by Run Length Encoding or losslessly compressing Light Detection and Ranging output data and rapidly accessing this compressed data which is filtered by attributes without the need to read or decompress the entire collection of data.

Quadtree Driven Lossy Event Compression

Abstract: With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression algorithm is far from satisfying and practical. The main challenge for compressing events is the unique event data form, i.e., a stream of asynchronously fired event tuples each encoding the 2D spatial location, timestamp, and polarity (denoting an increase or decrease in brightness). Since events only encode temporal variations, they lack spatial structure which is crucial for compression. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our Poisson Disk Sampling based Lossy Event Compression (PDS-LEC) algorithm performs rate-distortion based optimal allocation. On average, our algorithm achieves greater than 6x compression compared to the state of the art.