  Typical convolutional networks are trained and conducted on RGB images.
However, images are often compressed for memory savings and efficient
transmission in real-world applications. In this paper, we explore methods for
performing semantic segmentation on the discrete cosine transform (DCT)
representation defined by the JPEG standard. We first rearrange the DCT
coefficients to form a preferred input type, then we tailor an existing network
to the DCT inputs. The proposed method has an accuracy close to the RGB model
at about the same network complexity. Moreover, we investigate the impact of
selecting different DCT components on segmentation performance. With a proper
selection, one can achieve the same level accuracy using only 36% of the DCT
coefficients. We further show the robustness of our method under the
quantization errors. To our knowledge, this paper is the first to explore
semantic segmentation on the DCT representation.
