Asymmetric Multi-Layer Compression: Decoupling Inter-Layer Coding Dependencies With a Learned Model

Multi-layer compression is employed to achieve scalability by providing a base bitstream and additional enhancement layers to be used when extra bandwidth is available. In this work, we combine handcrafted and learned solutions, isolating the former in the base layer and using the latter in the enhancement layer. Moreover, the proposed Asymmetric Multi-layer Compression (AMLC) model decouples the base and enhancement layers at the encoding side, leveraging the end-to-end enhancement layer model to simplify the coding process. AMLC encodes an image 1.28x faster than a fully learned multi-layer codec while maintaining similar quality and coding efficiency despite the lower quality of the handcrafted base layer used in AMLC. While the proposed asymmetric encoder together with the handcrafted base layer reduce the overall complexity, the learned enhancement layer brings benefits in terms of coding efficiency. AMLC outperforms the coding efficiency of the Scalable HEVC (SHVC) reference software, a handcrafted multi-layer codec, achieving BD-Rate gains of-27.86%,-34.19%, and-26.57% for PSNR-YUV, PSNR-RGB, and MS-SSIM quality metrics, respectively. Finally, an equivalent implementation of the proposed model adding the inter-layer dependency on the encoding side produces results that are similar to the ones from AMLC's, confirming its potential to simplify multi-layer compression without significant losses in coding efficiency.