Frame Level Content Adaptive λ for Neural Video Compression

Zhirui Zuo, Junqi Liao, Xiaomin Song, Zheng Liu, Huiming Zheng, Dong Liu

Abstract: Neural video compression (NVC) methods have made significant advances in recent years. In most NVC methods, all frames share the same Rate-Distortion trade-off parameter λ, which might be sub-optimal. Recently, inspired by traditional video codecs' hierarchical quality structure, DCVC-DC proposed allocating periodic weights to λ to equip NVC with the hierarchical quality structure. However, the inspiration from traditional video codecs is designed to complement their complex reference structure. Compared to traditional video codecs, NVC methods' reference structure is much simpler and may not require large fluctuations in their hierarchical quality structure. Moreover, DCVC-DC's fixed hierarchical quality structure ignored the influence of video content. We conduct an elaborate study on the hierarchical quality structure in DCVC-DC, shedding light on the potential for improving compression performance by proposing a content adaptive λ to achieve a more reasonable hierarchical quality structure based on the fixed hierarchical weights. Experimental results demonstrate that the proposed method achieves a better rate-distortion performance than allocating the fixed weights to the fixed λ. On DCVC-DC and DCVC-SDD, we achieved 4.9% and 8.8% bdrate reduction with our method.