activity
20242026
most citedCausal Context Adjustment Loss for Learned Image Compression

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression

Shiyin Jiang, Wei Long, Minghao Han +3

The rapid growth of visual data under stringent storage and bandwidth constraints makes extremely low-bitrate image compression increasingly important. While Vector Quantization (V…

eess.IV2025

Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution

Minghao Han, Weiyi You, Jinhua Zhang +3

While learned image compression (LIC) focuses on efficient data transmission, generative image compression (GIC) extends this framework by integrating generative modeling to produc…

cs.CV2025★ 1 cited

MVAR: Visual Autoregressive Modeling with Scale and Spatial Markovian Conditioning

Jinhua Zhang, Wei Long, Minghao Han +2

Essential to visual generation is efficient modeling of visual data priors. Conventional next-token prediction methods define the process as learning the conditional probability di…

cs.CV2025

Neural Stereo Video Compression with Hybrid Disparity Compensation

Shiyin Jiang, Zhenghao Chen, Minghao Han +1

Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can be broadly categorized into two…

eess.IV2024★ 1 cited

Causal Context Adjustment Loss for Learned Image Compression

Minghao Han, Shiyin Jiang, Shengxi Li +4

In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniq…