4 accepted papers
Learned image compression (LIC) methods surpass traditional algorithms in rate-distortion (RD) performance, but still struggle to optimally balance effectiveness and efficiency. Moreover, although recent studies have demonstrated the effectiveness of utilizing frequency-domain information, they typi
Learning-based lossless compressors have been validated to have competitive advantages in genomics data (GD) compression. However, learning-based GD-dedicated compressors typically need to be pre-trained on multi-source data and then are directly used to compress another target data, we denote them…
Learning-based compression shows competitive compression ratios for genomics data. It often includes three types of compressors: static, adaptive and semi-adaptive. However, these existing compressors suffer from inferior compression ratios or throughput, and adaptive compressors also faces model c…
Explosive growth of multi-source data (MSD) poses challenges in data transmitting and storing. Neural Network (NN)-based lossless compressors are an important type of compression approaches to alleviate these problems. However, existing NN-based lossless compressors suffer from poor compression rati…