# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
###### Abstract
Predictive techniques are typically integrated with transformations, such as utilizing DPCM for DC coefficients in the DCT within the JPEG, or applying DPCM to wavelet coefficients in JPEG 2000. This amalgamation allows for further compression by exploiting the inter-coefficient correlation. The intra prediction coding in video coding standards such as H.264/AVC and High Efficiency Video Coding (HEVC) also utilizes DPCM [13,14]. Dictionary-based compression methods dynamically build or reference predefined dictionaries of patterns, replacing repeated sequences with shorter symbols to achieve efficient compression without relying on transformation-specific knowledge. However, the data encoded by the dictionary may still contain redundancy, such as certain symbols or indices appearing more frequently. To address this, entropy coding is applied to further compress the data by assigning shorter codes to more frequently occurring symbols, such as in the Lempel-Ziv-Markov chain Algorithm (LZMA) and Deflate [15,16]. Dictionary-based lossless image compression has inherent limitations, as these methods rely on repeated patterns within the data and do not fully leverage spatial redundancy in images, which makes them less effective in eliminating spatial redundancy. Furthermore, dictionary encoding lacks sufficient capability to handle fine details. These limitations become particularly evident in 14-bit line-scan panoramic images, which feature a large number of source symbols, a wide symbol range, abundant details, complex structures, and rapidly changing scenes. Traditional image compression methods, such as JPEG, JPEG-LS, and JPEG 2000 [17, 18, 19, 20, 21], are designed to minimize perceived quality loss by the human visual system (HVS) while reducing data transmission rates to improve the efficiency of image transmission and storage. JPEG (Joint Photographic Experts Group) is a widely used lossy image compression standard that reduces file sizes by discarding information that is less noticeable to the human eye, often resulting in some loss of image quality. JPEG-LS (JPEG Lossless and Near-Lossless Compression), on the other hand, is a standard for lossless or near-lossless compression. The latter is a standard for lossless compression, which is a standard for lossless compression. lossless compression, providing higher compression ratios while maintaining the original image quality. In recent years, more advanced image compression schemes have been continuously proposed and developed. For instance, ref. [22] proposed a compressive sensing-based image compression system. Nevertheless, their advantages over JPEG are not significant enough to justify the creation of new standards. Image compression includes both lossy and lossless methods. JPEG is a lossy image compression method designed for lower bit-depth images, specifically for 8-bit images. Ref. [22] is essentially a lossy compression method. However, lossy compression may not be suitable for many imaging applications that require high precision, such as hyperspectral imaging and infrared weak target detection. Since lossless compression methods can fully recover the original data, they are more favored in these applications. JPEG 2000 offers lossless compression capabilities, achieving efficient compression at the cost of increased computational and memory resource consumption. Its complexity arises from the implementation of the 5/3 lifting wavelet transform, bit-plane coding, and MQ coding, which refers to a context-based arithmetic coding technique used to efficiently encode the quantized wavelet coefficients in the image, improving compression performance by exploiting the statistical dependencies between the coefficients. JPEG-LS achieves a low-complexity lossless compression that is easy to implement in hardware, using simple prediction, context modeling, and Golomb coding. This approach sacrifices compression efficiency in favor of speed improvement.