.png){kind=icon}
Return
End-to-End Optimized 360° Image Compression
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2022.3208429.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The 360 degrees image that offers a 360-degree scenario of the world is widely used in virtual reality and has drawn increasing attention. In 360 degrees image compression, the spherical image is first transformed into a planar image with a projection such as equirectangular projection (ERP) and then saved with the existing codecs. The ERP images that represent different circles of latitude with the same number of pixels suffer from the unbalance sampling problem, resulting in inefficiency using planar compression methods, especially for the deep neural network (DNN) based codecs. To tackle this problem, we introduce a latitude adaptive coding scheme for DNNs by allocating variant numbers of codes for different regions according to the latitude on the sphere. Specifically, taking both the number of allocated codes for each region and their entropy into consideration, we introduce a flexible regional adaptive rate loss for region-wise rate controlling. Latitude adaptive constraints are then introduced to prevent spending too many codes on the over-sampling regions. Furthermore, we introduce viewportbased distortion loss by calculating the average distortion on a set of viewports. We optimize and test our model on a large 360 degrees dataset containing 19, 790 images collected from the Internet. The experiment results demonstrate the superiority of the proposed latitude adaptive coding scheme. On the whole, our model outperforms the existing image compression standards, including JPEG, JPEG2000, HEVC Intra Coding, and VVC Intra Coding, and helps to save around 15% bits compared to the baseline learned image compression model for planar images.
Keywords:
360 degrees Learned image compression
equirectangular projection (ERP)
unbalanced sampling
latitude adaptive code allocation
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave View more