JPEG is a lossy compression technique which is the default format for all major digital cameras and estimated to be used for over 80% of all digital images on the web [1], [2]. During JPEG encoding, quantization is used to approximate frequency coefficients by a set of integers called the quantization table. We introduce OptQT as a new framework for the optimal design of JPEG quantization tables.

The “synchronization” property of Asymmetric Numeral Systems (ANS) is vital for massively parallel decoding on platforms like GPUs [2]. This allows a decoder, starting from an arbitrary position and state, to converge to the correct decoding sequence. While the synchronization of methods like Huffman coding is well-understood [1], a formal analysis for ANS has been missing. This work derives the single-step synchronization probability for ANS, a key metric for parallel performance.

In this paper, we address the limitations of current measures for estimating the lossless compression limits. Shannon entropy, while practical, assumes a known data distribution and does not account for the complexity of representing this distribution. Kolmogorov Complexity (KC) [2], on the other hand, offers a more complete measure by considering both data and model complexity, but it is uncomputable in practice. To bridge these gaps, we propose a novel framework that estimates lower and upper bounds for lossless compression limits, leveraging neural scaling laws [1] to balance model and data complexity. Our experiments demonstrate the accuracy of our approach on synthetic datasets, with an average estimation error of 1.18%, and highlight its effectiveness as a tool for evaluating lossless compression methods on real-world datasets.

We propose ICE, an efficient and intelligent system that utilizes our dual-algorithm strategy to achieve superior lossless block-level compression results compared to traditional single compressor methods, especially with intermediate compression levels within the supported range of compressors. ICE employs a multi-step architecture to sample, analyze, plan, and execute compression. Unlike many existing solutions, ICE enables the user to set lower bounds (referred to as ‘PIVOT’) on throughput (compression speed) and compression ratio based on real-world constraints. While meeting these bounds, ICE maximizes the user's desired dimension. Our experimental results over several datasets from different domains demonstrate that ICE can improve the total compression throughput and compression ratio of these datasets with an average of 84% and 1.3% respectively, based on the selected PIVOT value.

Scientific data often exhibits well-defined model mechanisms, which can be characterized by differential equations. However, existing compression algorithms face challenges in distinguishing between information that encapsulates underlying mechanisms and redundant information in the data. Accordingly, these algorithms struggle to accurately assess the true level of meaningful precision [1] . To address this issue, we propose a compression method that incorporates mechanism learning and effective precision identification. By locally characterizing the data’s spatial relationship with linear differential equations and minimizing the source term, we learn the mechanisms. This allows us to separate noise from the data while preserving its precision. Thus, detectable patterns are more likely to emerge, enabling effective compression [2] .

JPEG is a widely used format for images. Most JPEG variants are based upon a block-based DCT transformation followed by quantization and entropy coding. Redundancy at row/column level is explored in [1]. Brunsli [2] and Lepton [3], lossless JPEG repacking libraries, explore redundancy at block level.

Remote sensing are widely used in applications including geoexploration, topographic mapping and weather forecasting, producing vast amounts of multi and hyper-spectral image data that need to be compressed [1]. The data acquisition process often leads to artifacts in the form of stripes with unpredictable positions and amplitudes [2]. The stripes deteriorate the smoonthless of the original image, causing challenges for high-ratio lossless compression. This motivates us to propose a split-and-compress framework. Rather than direct compression, we split (decompose) the image into a smooth part and a sparse remainder (capturing the stripes and artifacts alike) and compress the two parts separately. The decomposition is achieved using a fast, robust statistics based method with linear computational complexity on the number of pixels.