Channel-Wise Progressive Learning For Lossless Image Compression

This paper presents a channel-wise progressive coding system for lossless compression of color images. We follow the classical lossless compression scheme of LOCO-I and CALIC, where pixel values and coding contexts are predicted and forwarded to the entropy coder for compression. The contribution is...

Full description

Saved in:
Bibliographic Details
Main Authors: Rhee, Hochang, Jang, Yeong Il, Kim, Seyun, Cho, Nam Ik
Format: Conference Proceeding
Language:English
Subjects:
Channel estimation
Codecs
Context modeling
Encoding
Entropy
Image coding
Image color analysis
Lossless Compression
Predictive Coding
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a channel-wise progressive coding system for lossless compression of color images. We follow the classical lossless compression scheme of LOCO-I and CALIC, where pixel values and coding contexts are predicted and forwarded to the entropy coder for compression. The contribution is that we jointly estimate the pixel values and coding contexts from neighboring pixels by training a simple multilayer perceptron in a residual and channel-wise progressive manner. Specifically, we obtain accurate pixel prediction along with coding contexts that reflect the magnitude of local activity very well. These results are sent to an adaptive arithmetic coder that appropriately encodes the prediction error according to the corresponding coding context. Experimental results demonstrate the effectiveness of the proposed method in high-resolution datasets.
ISSN:2381-8549
DOI:10.1109/ICIP40778.2020.9191322