1 citations · 1 across the 8 of their papers we have counts for
8 papers
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
Mohsen Jenadeleh, Jon Sneyers, João Ascenso +8
Recent advances in conventional and learning-based image coding have increased the demand for benchmark datasets that support fine-grained assessment of compressed image quality, p…
On Optimizing Image Codecs for VMAF NEG: Analysis, Issues, and a Robust Loss Proposal
Florian Fingscheidt, Alexander Karabutov, Panqi Jia +2
The VMAF (video multi-method assessment fusion) metric for image and video coding recently gained more and more popularity as it is supposed to have a high correlation with human p…
Overview of Variable Rate Coding in JPEG AI
Panqi Jia, Fabian Brand, Dequan Yu +3
Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-ba…
Adapting Learned Image Codecs to Screen Content via Adjustable Transformations
H. Burak Dogaroglu, A. Burakhan Koyuncu, Atanas Boev +2
As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performanc…
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
Panqi Jia, A. Burakhan Koyuncu, Jue Mao +9
The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in th…
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
Panqi Jia, Jue Mao, Esin Koyuncu +6
Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate…
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction
Esin Koyuncu, Timofey Solovyev, Johannes Sauer +2
Learned image compression has a problem of non-bit-exact reconstruction due to different calculations of floating point arithmetic on different devices. This paper shows a method t…
Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression
A. Burakhan Koyuncu, Panqi Jia, Atanas Boev +2
Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achi…