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New lossless compression method for BMP true color images
Published in Xiaoling Jia, Feng Wu, Electromechanical Control Technology and Transportation, 2017
BMP (Bitmap) is the standard image file format on Microsoft Windows operating system and it is a bitmap image file format. Typical color depths (the number of bits per pixel) of the BMP image include 1, 4, 8 and 24, which make the BMP image have abundant picture information (Song and Ye 2011), so the compression effect of BMP is not obvious. Therefore, BMP is limitedly used for the gray-scale image and the commonly used method is Run Length Encoding (RLE), which also has its own limitations. RLE can achieve ideal compression efficiency when the color information in the image has very high repetition. On the contrary, this compression method will only increase the data volume while any two adjacent pixels are not same. BMP never provides any compression method for the true color image to save the storage space. Therefore, designing a fast and efficient lossless compression method to extend the application range of the BMP file is of great interest.
Image Data Formats and Image Compression
Published in Elizabeth Berry, A Practical Approach to Medical Image Processing, 2007
In RLE coding, this would be replaced by: 191|3 192|2 0|6, which says that there are three 191s, then two 192s and 6 zeroes. Instead of the 11 bytes required for the original list of pixel values, we need only 6 bytes for storage. Note that: RLE can give compression ratios of about 1.5:1 on grayscale imagesIt is a lossless method, as it will result in the reconstruction of exactly the same pixel values as in the original image.RLE reduces the interpixel redundancy in an image.
Digital picture compression
Published in Steve Heath, Multimedia and Communications Technology, 1999
This simple scheme only supports run lengths of up to 9 characters—10 if 0 is used as a valid number for the repeat value — but it can be expanded if needed. This technique, known as run length encoding or RLE, is frequently used to compress data — especially where there are large amounts of consecutive data with the same value.
Feature Selection for Supervised Learning and Compression
Published in Applied Artificial Intelligence, 2022
Phillip Taylor, Nathan Griffiths, Vince Hall, Zhou Xu, Alex Mouzakitis
The DWT operates by extracting two signals that are each half the length of the original (Addison 2017). The first represents an approximation of the original signal and is referred to as the low frequency (LF) component. The second is the high frequency (HF) component and is a representation of the detail in the original signal. The LF and HF components are produced by a convolution of the original signal with wavelet kernels, followed by a down-sampling by a factor of 2. Typically, the HF component contains many small values and can be considered the noise component of the original signal. By quantizing this component and applying a threshold, many of these small values become zero and can be encoded very efficiently using lossless compression methods such as run-length encoding. This quantization introduces errors into the signal when it is reconstructed, but the error is minimized because only the HF component (detail) is affected. This process can be performed recursively to the LF component, further increasing the potential for lossless compression of the coefficients.