The recent developments in the research area highlight a significant focus on enhancing communication and image processing technologies through innovative quantization, compression, and coding strategies. A notable trend is the exploration of advanced quantization techniques in communication systems, aiming to optimize performance under various constraints such as resolution and gain control. In the realm of image processing, there's a push towards more efficient and adaptable compression methods, with a particular emphasis on variable-length tokenization and loss-resilient coding to improve reconstruction quality and adaptability across different applications. Additionally, the field is witnessing the application of reinforcement learning and information-theoretic frameworks to maximize system performance and understand the fundamental limits of data compression and summarization.

Noteworthy Papers

• A High-Resolution Analysis of Receiver Quantization in Communication: Introduces an analytical form for achievable rate under quantization, emphasizing the importance of gain control and providing asymptotic results for rate loss.
• One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression: Presents a novel image tokenizer supporting variable-length tokenization, significantly improving compression efficiency and reconstruction quality.
• Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC: Develops a reinforcement learning approach to adapt feedback schemes in URLLC, enhancing goodput under dynamic channel conditions.
• Towards Loss-Resilient Image Coding for Unstable Satellite Networks: Proposes a loss-resilient image coding method for satellite networks, demonstrating superior performance in challenging environments.
• The Gap Between Principle and Practice of Lossy Image Coding: Identifies and quantifies the gap between theoretical and empirical rate-distortion functions in image coding, highlighting areas for future improvement.