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Smaller and Faster 3DGS via Post-Training Dictionary Learning

arXiv:2605.30396v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is a promising neural scene representation for real-time rendering, but trained models often suffer from large memory footprints, limiting deployment on less powerful devices. Existing compression techniques often lead to architectures with several additional trainable parameters. While achieving outstanding compression ratios, they introduce noticeable drops in image quality.

arXiv CS 9d ago

Residual Modeling for High-Fidelity Learned Compression of Scientific Data

arXiv:2606.05389v1 Announce Type: new Abstract: Lossy compression is essential for massive spatiotemporal data from scientific simulations. Learned compressors can achieve high compression ratios at moderate accuracy targets, but their aggregate reconstruction losses do not guarantee accuracy for each block. Existing Guaranteed Autoencoder (GAE) methods add a per-block residual correction by retaining SVD/PCA-style coefficients until the target is met.

arXiv CS 5d ago

Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation

Announce Type: new Abstract: We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dynamically filter, abstract, and compress any-length history at constant cost. Existing methods mainly curate memory with predefined KV-cache schedules, fixed-ratio heuristic compression, or inference-time RoPE adaptation. These designs inevitably lose historical information and amplify compounding errors due to their...

arXiv CS 6d ago

NormEval: A Unified Multi-Metric Framework for Evaluating Semantic Fidelity in Text Normalization

arXiv:2511.20409v2 Announce Type: replace Abstract: Text normalization methods such as stemming and lemmatization are fundamental components of NLP pipelines. As new normalization tools are developed for diverse languages, evaluation methodologies remain fragmented, relying on Compression Ratio, downstream accuracy, or sequence-to-sequence prediction scores in isolation, failing to distinguish between beneficial vocabulary reduction and harmful semantic distortion.

arXiv CS 8d ago

Ultra-Fast Neural Video Compression

arXiv:2606.04410v1 Announce Type: new Abstract: While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to real-world deployment. This paper introduces a chunk-based coding framework designed to significantly improve the rate-distortion-complexity trade-off. Instead of processing frames sequentially, our approach encodes a chunk of multiple frames into a single compact latent representation and...

arXiv CS 6d ago

Kore: Binary File Format Optimized for Modern Data Systems (Open Source)

The fastest, most compressed columnar format for big data | v0.1.0 KORE is a high-performance binary file format optimized for analytical workloads. It provides: - 38% compression ratio (vs 63% for Parquet) - 131x query speedup with column pruning & predicate pushdown - Zero data loss verification (400K+ cells tested) - Native Spark integration — read/write with PySpark Add this crate as a dependency (when published) or include from path: use kore_fileformat::*; // Write data...

Hacker News 11d ago

A One-Dimensional Discrete Boltzmann Method for Multidimensional Compressible Flows

arXiv:2603.01546v2 Announce Type: replace Abstract: A simple and efficient one-dimensional discrete Boltzmann method is developed for compressible flows with tunable specific heat ratios by incorporating extra degrees of freedom. To guarantee Galilean invariance in numerical simulations, a discrete velocity set is constructed with high spatial symmetry. Furthermore, an operator-splitting scheme is proposed to extend the one-dimensional kinetic formulation to simulations of one-, two-, and...

arXiv Physics 1d ago

TextEconomizer: Enhancing Lossy Text Compression with Denoising Transformers and Entropy Coding

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arXiv CS 1d ago

Spatial Artifact Coherence Determines Codec Robustness in Patch-Based rPPG

arXiv:2606.04198v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) achieves low heart-rate error on uncompressed benchmarks yet is deployed over compressed video channels in telehealth, neonatal ICU, and driver fatigue applications. No prior work identifies the physical quantity determining when spatial decomposition outperforms global-projection methods under codec compression. We propose Spatial Artifact Coherence (SAC), defined as the ratio of off-diagonal to diagonal...

arXiv CS 6d ago

ACEAPEX: Parallel LZ77 Decoding via Encode-Time Absolute Offset Resolution

arXiv:2606.04268v1 Announce Type: new Abstract: LZ77-based codecs exhibit a fundamental sequential bottleneck in decoding: each back-reference depends on previously decompressed data, preventing multi-core scaling. We present ACEAPEX, a parallel LZ77 codec that stores all back-references as absolute positions in the decompressed output and organizes data into self-contained 1 MB blocks, enabling embarrassingly parallel block-level decoding. Integrated into lzbench, ACEAPEX achieves 10,160...

arXiv CS 6d ago