Topic
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.
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19 Sep 2019
TL;DR: In this article, a system for processing spatial data may be designed to receive neural network outputs corresponding to a first spatial data set, and translate the neural network output corresponding to the first spatial dataset to the second spatial dataset based on the motion between the first and second spatial datasets.
Abstract: A system for processing spatial data may be designed to receive neural network outputs corresponding to a first spatial data set, and translate the neural network outputs corresponding to the first spatial data set based on the motion between a second spatial data set and the first spatial data set. The system may perform zero-gap run length encoding on the neural network outputs to store the neural network outputs in memory. The system may also perform on-the-fly skip zero decoding and bilinear interpolation to translate the neural network outputs.
01 Aug 2015
TL;DR: This paper presents a fast implementation of multi-band blending for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion, and presents detailed quantitative results compared with Open CV and Enblend to demonstrate the speed and memory improvements.
Abstract: This paper presents a fast implementation of multi-band blending for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. We first compute a unique seam image using two-pass nearest distance transform, which is independent on the order of input images and has good scalability. Each individual mask can be extracted from this seam image quickly. To promote execution speed and reduce memory usage in building large area mosaics, the seam image and masks are compressed using run-length encoding, and all the following mask operations are built on run-length encoding scheme. We apply our fast blending system to large scale data sets and present detailed quantitative results compared with Open CV and Enblend to demonstrate the speed and memory improvements.
01 Jan 2019
TL;DR: This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards that surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).
Abstract: This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).
17 Oct 2018
TL;DR: The Huffman algorithm and Run Length Encoding aimed to compress audio files *.mp3 and *.wav so that the size of the compressed file was smaller than the original file where the parameter used to measure the performance of this algorithm was the compression ratio, and the resulting complexity.
Abstract: Penelitian ini dilakukan untuk menganalisis perbandingan hasil kompresi dan dekompresi file audio*.mp3 dan *.wav. Kompresi dilakukan dengan mengurangi jumlah bit yang diperlukan untuk menyimpan atau mengirim file tersebut. Pada penelitian ini penulis menggunakan algoritma Huffman dan Run Length Encoding yang merupakan salah satu teknik kompresi yang bersifat lossless.Algoritma Huffman memiliki tiga tahapan untuk mengkompres data, yaitu pembentukan pohon, encoding dan decodingdan berkerja berdasarkan karakter per karakter. Sedangkan teknik run length ini bekerja berdasarkan sederetan karakter yang berurutan, yaitu hanya memindahkan pengulangan byte yang sama berturut-turut secara terus-menerus. Implementasi algoritma Huffman dan Run Length Encoding ini bertujuan untuk mengkompresi file audio *.mp3 dan *.wav sehingga ukuran file hasil kompresi lebih kecil dibandingkan file asli dimana parameter yang digunakan untuk mengukur kinerja algoritma ini adalah rasio kompresi, kompleksitas yang dihasilkan. Rasio kompresi file audio *.mp3 menggunakan Algoritma Huffman memiliki rata-rata 1.204% sedangkan RLE -94.44%, dan rasio kompresi file audio *.wav memiliki rata-rata 28.954 % sedangkan RLE -45.91%.
This research was conducted to analyze the comparison of the results of compression and decompression of *.mp3 and *.wav audio files. Compression was completed by reducing the number of bits needed to save or send the file. In this study, the researcher used the Huffman algorithm and Run Length Encoding which is one of the lossless compression techniques. The Huffman algorithm has three stages to compress data, namely tree formation, encoding and decoding which work based on characters per character. On the other hand, the run length technique works based on a sequence of sequential characters that only move the repetitions of the same byte in succession continuously. The implementation of the Huffman algorithm and Run Length Encoding aimed to compress audio files *.mp3 and *.wav so that the size of the compressed file was smaller than the original file where the parameter used to measure the performance of this algorithm was the compression ratio, and the resulting complexity.*.Mp3 audio file compression ratio using Huffman Algorithm had an average of 1.204% while RLE -94.44%, and compression ratio *.wav audio files had an average of 28.954% while RLE -45.91%.
03 Dec 2018
TL;DR: The application of Run Length Encoding (RLE) algorithm in image compression cannot always reduce the size of the image compression results as mentioned in this paper, however, RLE has a special ability to reduce image files from the others if there is a composition of the value of repetitive image pixels.
Abstract: The application of Run Length Encoding (RLE) algorithm in image compression cannot always reduce the size of the image compression results. Giving a Run sign or the number of pixels that have repeated succession can certainly change the size of the image file to be smaller, but very different from the repetitive image pixels but not sequential or not at all will certainly give a large size change in the file compression Image compression files that use Algorithm RLE in applications that are often used by users in general on computers can read image matrices. Thus RLE has a special ability to reduce image files from the others if there is a composition of the value of repetitive image pixels. And to decompress RLE to digital images is also very simple because the file type .rle has information on the order of matrix values consisting of two parts, the odd order is the pixel value for the image and while the even order is the value of the number of repeaters in the previous odd pixel value.