# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
###### Abstract  
Traditional JPEG series image compression algorithms have limitations in speed. To improve the storage and transmission of 14-bit/pixel images acquired by infrared line-scan detectors, a novel method is introduced for achieving high-speed and highly efficient compression of line-scan infrared images. The proposed method utilizes the features of infrared images to reduce image redundancy and employs improved Huffman coding for entropy coding. The improved Huffman coding addresses the low-probability long coding of 14-bit images by truncating long codes, which results in low complexity and minimal loss in the compression ratio. Additionally, a method is proposed to obtain a Huffman code table that bypasses the pixel counting process required for entropy coding, thereby improving the compression speed. The final implementation is a low-complexity lossless image compression algorithm that achieves fast encoding through simple table lookup rules. The proposed method results in only a 10% loss in compression performance compared to JPEG 2000, while achieving a 20-fold speed improvement. Compared to dictionary-based methods, the proposed method can achieve high-speed compression while maintaining high compression efficiency, making it particularly suitable for the high-speed, high-efficiency lossless compression of line-scan panoramic infrared images. The code table compression effect is 5% lower than the theoretical value. The algorithm can also be applied to analyze images with more bits. image lossless compression; Huffman coding; information entropy; line-scan infrared panoramic images; JPEG +
Footnote †: journal: Applied sciences  
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pixel bit depth, high-rate data transmission and storage face corresponding challenges. For example, a 14-bit line-scan image with 3072 pixels per column and approximately 60,000 columns per panoramic image has an imaging cycle of about 25 microseconds per column, leading to an imaging rate of 240 MB/s and a frame size of 350 MB. Lossless data compression, which can reduce data redundancy, is essential for improving data transmission and storage efficiency. Image compression methods leverage the natural compressibility of images by utilizing correlations between adjacent pixels to reduce redundancy and concentrate information. Finally, entropy coding is applied to remove coding redundancy, further achieving compression. Image transformation and prediction are widely used techniques for reducing image redundancy. To complement these approaches, dictionary-based compression methods, such as Lempel-Ziv-Welch (LZW), Lempel-Ziv 77 (LZ77), and Lempel-Ziv 78 (LZ78), are also employed [2,3]. These methods reduce redundancy by identifying and encoding repeated patterns or sequences in the image data, which can be especially effective for certain types of image content. Transform-based image compression converts images into a domain with less redundancy, where information is concentrated and easier to encode. The image is usually transformed into the frequency or spatial domain and then encoded using the properties of the transform coefficients, such as image compression based on discrete wavelet transform (DWT) [4, 5, 6], discrete cosine transform (DCT) [7], and integer discrete Tchebichef transform [8]. Prediction-based image compression exploits the correlation between pixels. The residual image obtained by subtracting the original image from the predicted image contains less redundant information. The residual image typically has a narrow range of pixel values, making it more effectively compressed through entropy coding techniques such as Huffman coding [9] or arithmetic coding [10]. Typical prediction-based compression includes DPCM [11] and LOCO-I [12].