Deep image compression with lifting scheme: Wavelet transform domain based on high-frequency subband prediction

Image compression is the most important image processing method extensively deployed in different appliances. "Discrete wavelet transform (DWT)" is one of the well-adopted transforming methods exploited for compressing images. The extremely deployed version of DWT is convolution-oriented. Nevertheless, the lifting-oriented DWT scheme requires more contemplation on more proficient performance and lesser computation cost. This paper intends to propose a deep learning-based image compression model with a lifting scheme for predicting high-frequency subbands. Moreover, the fine-tuning in lifting factorization is done by a new Sea Lion with Averaged Update Evaluation that includes new cosine estimation under the COordinate Rotation DIgital Computer algorithm. Similarly, this study defines a new single objective function that merges the multiconstraints, like, "Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR)". At last, the supremacy of the presented approach is proved with respect to varied measures, like, CR, PSNR and so on.