2019/07/23 by Shao-Yuan Lo, Lo, Shao-Yuan, Hsueh‐Ming Hang +2 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Image and Signal Denoising Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1907.10015
Accepted in ACM International Conference on Multimedia in Asia (MMAsia) 2019
openalex publication_date 2019/07/23 · arxiv created 2019/12/29 · arxiv updated 2020/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.