Learned Lossless Image Compression Based on Optimal Kernel Transformer Approach

Image compression is crucial for reducing storage and transmission costs, particularly in applications involving high-resolution and complex imagery. Traditional compression methods, such as JPEG, PNG, and newer lossless formats like JPEG XL and WebP, often suffer from suboptimal compression ratios (CRs) and image quality when handling modern high-definition content. To overcome these limitations, this paper proposes a novel deep learning-based lossless image compression method. The symmetrical transformer (STF) model is introduced, integrating transformer blocks in both the downsampling encoder and upsampling decoder to enhance the capture of local and global features. The model also includes a multivariate mixture distribution channel conditioning (MMCC) entropy model, which improves pixel dependency predictions by modeling complex relationships within image channels. Additionally, an automated searching of optimal kernel shapes (SOKS) is employed to dynamically configure kernel sizes, optimizing the convolutional layers for different image regions. The system also applies stripe-wise pruning (SWP), which selectively prunes unimportant features during compression, reducing computational complexity and memory usage without compromising image quality. Extensive evaluations on standard datasets, including Kodak, CLIC, and DIV2K, demonstrate the effectiveness of the proposed model. Specifically, the approach achieves significant compression efficiency, with bits-per-dimension (BPD) values of 3.06 on Kodak, 3.91 on CLIC, and 3.63 on DIV2K, outperforming traditional methods such as iVPF (3.20 BPD on Kodak), GOLLIC (3.15 BPD on Kodak), and MSPSM (3.12 BPD on Kodak). In addition to compression efficiency, the proposed method excels in inference speed, with encoding times as low as 7.80 ms per sample on Kodak, significantly faster than competing methods. These results demonstrate a substantial improvement in compression rates and image reconstruction quality, highlighting the model's potential for real-world applications, including medical imaging, remote sensing, and real-time streaming, offering a significant advancement in lossless image compression.