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HDR Images Compression
Published in Francesco Banterle, Alessandro Artusi, Kurt Debattista, Alan Chalmers, Advanced High Dynamic Range Imaging, 2017
Francesco Banterle, Alessandro Artusi, Kurt Debattista, Alan Chalmers
The focus of this section is on the compression of HDR textures, which are images used in computer graphics for increasing details of materials in a three-dimensional scene. The main difference between compressed textures and images is that the fetch of a pixel value has to happen in constant time allowing random access to the information. The drawback in having a random access is the limit in compression rates, around 8 bpp, because spatial redundancy is not fully exploited. Note that in the methods of the previous section, the full image needs to be decoded before providing access to the pixels, while with these texture methods this is not the case. Most of the texture methods are based on Block Truncation Coding (BTC) [170]. BTC is a compression scheme that divides the image in 4 × 4 pixels blocks. For each block the average value, m, is calculated and each pixel, x, is encoded as 0 if x < m, and as 1 if not. Then, the means of each group of pixels is calculated and stored. During the encoding, the mean of each group is assigned to their pixels; see Figure 8.3.
Image Compression
Published in Scott E. Umbaugh, Digital Image Processing and Analysis, 2017
BTC works by dividing the image into small subimages, or blocks, and then reducing the number of gray levels within each block. This reduction is performed by a quantizer that adapts to the local image statistics. The levels for the quantizer are chosen to minimize a specified error criterion, and then all the pixel values within each block are mapped to the quantized levels. The necessary information to decompress the image is then encoded and stored. Many different BTC algorithms have been defined by using various types of quantization and error criteria, as well as various preprocessing and postprocessing methods. The more sophisticated algorithms provide better results, but with a corresponding increase in computational complexity.
Research and implementation of an image compression scheme based on the BTC algorithm
Published in Xiaoling Jia, Feng Wu, Electromechanical Control Technology and Transportation, 2017
The BTC image compression algorithm was proposed by Delp and Mitchell (1979), whose basic theory is that the original image is divided into nonoverlapping blocks, with each block quantized into two representative gray values and bitmaps so as to get a compression rate of 2 bits/pixel. The BTC algorithm is simple and easy to implement. It can realize fast encoding and decoding and result in good compression image quality. However, the traditional BTC algorithm has a high pixel bit rate, which greatly limits the BTC algorithm in the current image compression applications. Therefore, in the following research, people focus on how to reduce the image’s pixel bit rate.
Content-based image retrieval using block truncation coding based on edge quantization
Published in Connection Science, 2020
Yan-Hong Chen, Ching-Chun Chang, Cheng-Yi Hsu
Because of the high volume of image information, many researchers have proposed various image retrieval schemes based on compression to reduce the capacity of the image in order to improve the retrieval effectiveness. As a fast image compression method, Block Truncation Coding (BTC) has the capability to perform the task of image retrieval efficiently. In 2003, Qiu (2003) presented an image retrieval approach using the features derived directly from the block truncation coding compressed stream. Gahroudi and Sarshar (2007) introduced an improved image retrieval method based on BTC in which a colour histogram and a block pattern histogram were combined. Silakari et al. (2009) proposed an image retrieval scheme focused on the colour feature using BTC and K-means clustering algorithm. Yu et al. (2011) proposed an effective colour image scheme retrieval based on BTC and vector quantization. In this method, each input colour image is decomposed into , and components.