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Found 8 papers, 0 papers with code
Adapting Learned Image Codecs to Screen Content via Adjustable Transformations
As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications.
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed.
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
Currently, there is a high demand for neural network-based image compression codecs.
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction
Learned image compression has a problem of non-bit-exact reconstruction due to different calculations of floating point arithmetic on different devices.
Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression
Entropy estimation is essential for the performance of learned image compression.
Learning-Based Conditional Image Coder Using Color Separation
Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction.
Device Interoperability for Learned Image Compression with Weights and Activations Quantization
Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance.
Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression
Entropy modeling is a key component for high-performance image compression algorithms.
