Non-autoregressive approaches, especially those that generate output in a one-pass forward manner, have shown great potential in improving the inference speed of translation models. However, these approaches often suffer from a significant drop in translation quality compared to autoregressive models (AT). To tackle this challenge, this paper introduces a series of innovative techniques to enhance the translation quality of non-autoregressive neural machine translation (NAT) models while still maintaining a substantial acceleration in inference speed. Specifically, we propose a method called CTCPMLM, which involves fine-tuning Pretrained Multilingual Language Models (PMLMs) with the Connectionist Temporal Classification (CTC) loss to effectively train NAT models. Additionally, we adopt the MASK insertion scheme instead of token duplication for up-sampling and present an embedding distillation method to further enhance the performance of NAT models. In our experiments, CTCPMLM surpasses the performance of the baseline autoregressive model (Transformerbase) on various datasets, including WMT'14 DE$\leftrightarrow$EN, WMT'16 RO$\leftrightarrow$EN, and IWSLT'14 DE$\leftrightarrow$EN. Moreover, CTCPMLM represents the current state-of-the-art among NAT models. Notably, our model achieves superior results compared to the baseline autoregressive model on the IWSLT'14 En$\leftrightarrow$De and WMT'16 En$\leftrightarrow$Ro datasets, even without using distillation data during training. Particularly, on the IWSLT'14 DE$\rightarrow$EN dataset, our model achieves an impressive BLEU score of 39.93, surpassing AT models and establishing a new state-of-the-art. Additionally, our model exhibits a remarkable speed improvement of 16.35 times compared to the autoregressive model.

This paper presents a novel intra prediction method for inter pictures (i.e. P pictures and B pictures), denominated enhanced intra prediction (EIP). The traditional intra prediction only uses the reconstructed pixels to the left and above to derive intra prediction blocks. While the proposed method combines the left-above and right-below pixels to strengthen the prediction efficiency of the intra blocks in inter pictures. For accessing the pixels below and to the right of intra coding units (CUs), the encoding and decoding structures are adjusted to guarantee that all the inter CUs are reconstructed before all the intra CUs. With more available reference samples around the intra CUs, EIP achieves better prediction results. The proposed method is implemented on top of the H.265/HEVC reference software (HM-16.12), and the experimental results show that approximate 0.5% BD-rate reduction is achieved under Random Access (RA) and Low Delay P (LP) configurations.

Panoramic video provides an immersive experience by presenting a 360° spherical video content. Due to the limitations of coding and storage technology, the spherical panoramic video needs to be projected onto the two-dimensional plane for storage and encoding. In this paper, we propose a polar square projection scheme. We project the area near the poles of the sphere into two square planes and a latitude circle on sphere is projected to a square circle on squares plane, in addition, the rest of area on sphere is projected into a rectangle by means of equal area projection. Experimental results show our proposed projection can obtain a gain of 11.63% BD-rate compared to the equirectangular projection.