AI

Geometry-driven quantization for omnidirectional image coding

Abstract

In this paper we propose a method to adapt the quantization tables of typical block-based transform codecs when the input to the encoder is a panoramic image resulting from equirectangular projection of a spherical image. When the visual content is projected from the panorama to the viewport, a frequency shift is occurring. The quantization can be adapted accordingly: the quantization step sizes that would be optimal to quantize the transform coefficients of the viewport image block, can be used to quantize the coefficients of the panoramic block. As a proof of concept, the proposed quantization strategy has been used in JPEG compression. Results show that a rate reduction up to 2.99% can be achieved for the same perceptual quality of the spherical signal with respect to a standard quantization.