9 accepted papers
Recently, learning-based image compression approaches have achieved superior performance over classical image compression methods. However, their complexities remain quite high. In this paper, we propose two efficient modules to reduce the complexity. First, we introduce a selective kernel residual…
"Recently learned image compression (LIC) has achieved great progress and even outperformed the traditional approach using DCT or discrete wavelet transform (DWT). However, LIC mainly reduces spatial redundancy in the autoencoder networks and entropy coding, but has not fully removed the frequency-d…
Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression. Despite impressive performance of end-to-end optimized compression with deep neural networks, the high…
Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks. In this paper, we propose a ROI-based image compression framework with Swin transformers as main building blo…
Multi-pass coding is a widely utilized technique to improve the compression efficiency in video coding, where frame statistics are collected from the previous passes and then analyzed to provide better encoder decisions, such as rate control parameters, prediction mode selection, motion estimation,…
Learned image compression has recently shown the potential to outperform the standard codecs. State-of-the-art rate-distortion (R-D) performance has been achieved by context-adaptive entropy coding approaches in which hyperprior and autoregressive models are jointly utilized to effectively capture t…
Bi-directional motion compensated prediction is widely utilized in video coding. Conventionally, the encoder searches for two motion vectors pointing to reference frames in both directions, and transmits these motion vectors to the decoder. Recognizing that the two reference frames are already avail…
Deep learning has revolutionized many computer vision fields in the last few years, including learning-based image compression. In this paper, we propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the segmentation map of the input image is obtained and e…
Transform coefficient coding is a key module in modern video compression systems. Typically, a block of the quantized coefficients are processed in a pre-defined zig-zag order, starting from DC and sweeping through low frequency positions to high frequency ones. Correlation between magnitudes of adj…