TVMC introduces a novel approach to compressing time-varying triangle meshes used in animation and simulations. Instead of treating each mesh frame independently, TVMC leverages temporal coherence by predicting vertex positions in subsequent frames based on previous ones. This prediction, combined with quantization and entropy coding, achieves significantly higher compression ratios compared to traditional methods, especially for meshes with smooth motion. The open-source implementation aims to be practical and efficient, enabling real-time decompression on consumer-grade hardware. It boasts a simple API and offers various parameters to control the trade-off between compression ratio and accuracy.
The Blend2D project developed a new high-performance PNG decoder, significantly outperforming existing libraries like libpng, stb_image, and lodepng. This achievement stems from a focus on low-level optimizations, including SIMD vectorization, optimized Huffman decoding, prefetching, and careful memory management. These improvements were integrated directly into Blend2D's image pipeline, further boosting performance by eliminating intermediate copies and format conversions when loading PNGs for rendering. The decoder is designed to be robust, handling invalid inputs gracefully, and emphasizes correctness and standard compliance alongside speed.
HN commenters generally praise Blend2D's PNG decoder for its speed and clean implementation. Some appreciate the detailed blog post explaining its design and optimization strategies, highlighting the clever use of SIMD intrinsics and the decision to avoid complex dependencies. One commenter notes the impressive performance compared to LodePNG, particularly for large images. Others discuss potential further optimizations, such as using pre-calculated tables for faster filtering, and the challenges of achieving peak performance with varying image characteristics and hardware platforms. A few users also share their experiences integrating or considering Blend2D in their projects.
The blog post "Zlib-rs is faster than C" demonstrates how the Rust zlib-rs crate, a wrapper around the C zlib library, can achieve significantly faster decompression speeds than directly using the C library. This surprising performance gain comes from leveraging Rust's zero-cost abstractions and more efficient memory management. Specifically, zlib-rs uses a custom allocator optimized for the specific memory usage patterns of zlib, minimizing allocations and deallocations, which constitute a significant performance bottleneck in the C version. This specialized allocator, combined with Rust's ownership system, leads to measurable speed improvements in various decompression scenarios. The post concludes that careful Rust wrappers can outperform even highly optimized C code by intelligently managing resources and eliminating overhead.
Hacker News commenters discuss potential reasons for the Rust zlib implementation's speed advantage, including compiler optimizations, different default settings (particularly compression level), and potential benchmark inaccuracies. Some express skepticism about the blog post's claims, emphasizing the maturity and optimization of the C zlib implementation. Others suggest potential areas of improvement in the benchmark itself, like exploring different compression levels and datasets. A few commenters also highlight the impressive nature of Rust's performance relative to C, even if the benchmark isn't perfect, and commend the blog post author for their work. Several commenters point to the use of miniz, a single-file C implementation of zlib, suggesting this may not be a truly representative comparison to zlib itself. Finally, some users provided updates with their own benchmark results attempting to reconcile the discrepancies.
This paper introduces a method for compressing spectral images using JPEG XL. Spectral images, containing hundreds of narrow contiguous spectral bands, are crucial for applications like remote sensing and cultural heritage preservation but pose storage and transmission challenges. The proposed approach leverages JPEG XL's advanced features, including its variable bit depth and multi-component transform capabilities, to efficiently compress these high-dimensional datasets. By treating spectral bands as image components within the JPEG XL framework, the method exploits inter-band correlations for superior compression performance compared to existing techniques like JPEG 2000. The results demonstrate significant improvements in both compression ratios and perceptual quality, especially for high-bit-depth spectral data, paving the way for more efficient handling of large spectral image datasets.
Hacker News users discussed the potential benefits and drawbacks of using JPEG XL for spectral images. Several commenters highlighted the importance of lossless compression for scientific data, questioning whether JPEG XL truly delivers in that regard. Some expressed skepticism about adoption due to the complexity of spectral imaging and the limited number of tools currently supporting the format. Others pointed out the need for efficient storage and transmission of increasingly large spectral datasets, suggesting JPEG XL could be a valuable solution. The discussion also touched upon the broader challenges of standardizing and handling spectral image data, with commenters mentioning existing formats like ENVI and the need for open-source tools and libraries. One commenter also shared their experience with spectral reconstruction from RGB images in the agricultural domain, highlighting the need for specific compression for such work.
Fast-PNG is a JavaScript library offering high-performance PNG encoding and decoding directly in web browsers and Node.js. It boasts significantly faster speeds compared to other JavaScript-based PNG libraries like UPNG.js and PNGJS, achieving this through optimized WASM (WebAssembly) and native implementations. The library focuses solely on PNG format and provides a simple API for common tasks such as reading and writing PNG data from various sources like Blobs, ArrayBuffers, and Uint8Arrays. It aims to be a lightweight and efficient solution for web developers needing fast PNG manipulation without large dependencies.
Hacker News users discussed fast-png's performance, noting its speed improvements over alternatives like pngjs, especially in decoding. Some expressed interest in WASM compilation for browser usage and potential integration with other projects. The small size and minimal dependencies were praised, and correctness was a key concern, with users inquiring about test coverage and comparisons to libpng's output. The project's permissive MIT license also received positive mention. There was some discussion about specific performance bottlenecks, potential for further optimization (like SIMD), and the tradeoffs of pure JavaScript vs. native implementations. The lack of interlaced PNG support was also noted.
The author benchmarks Rust's performance in text compression, specifically comparing it to C++ using the LZ4 and Zstd algorithms. They find that Rust, while generally performant, struggles to match C++'s speed in these specific scenarios, particularly when dealing with smaller input sizes. This performance gap is attributed to Rust's stricter memory safety checks and its difficulty in replicating certain C++ optimization techniques, such as pointer aliasing and specialized allocators. The author concludes that while Rust is a strong choice for many domains, its current limitations make it less suitable for high-performance text compression codecs where matching C++'s speed remains a challenge. They also highlight that improvements in Rust's tooling and compiler may narrow this gap in the future.
HN users generally disagreed with the premise that Rust is inadequate for text compression. Several pointed out that the performance issues highlighted in the article are likely due to implementation details and algorithmic choices rather than limitations of the language itself. One commenter suggested that the author's focus on matching C++ performance exactly might be misplaced, and optimizing for Rust's idioms could yield better results. Others highlighted successful compression projects written in Rust, like zstd, as evidence against the author's claim. The most compelling comments centered on the idea that while Rust's abstractions might add overhead, they also bring safety and maintainability benefits that can outweigh performance concerns in many contexts. Some commenters suggested specific areas for optimization, such as using SIMD instructions or more efficient data structures.
Succinct data structures represent data in space close to the information-theoretic lower bound, while still allowing efficient queries. The blog post explores several examples, starting with representing a bit vector using only one extra bit beyond the raw data, while still supporting constant-time rank and select operations. It then extends this to compressed bit vectors using Elias-Fano encoding and explains how to represent arbitrary sets and sparse arrays succinctly. Finally, it touches on representing trees succinctly, demonstrating how to support various navigation operations efficiently despite the compact representation. Overall, the post emphasizes the power of succinct data structures to achieve substantial space savings without significant performance degradation.
Hacker News users discussed the practicality and performance trade-offs of succinct data structures. Some questioned the real-world benefits given the complexity and potential performance hits compared to simpler, less space-efficient solutions, especially with the abundance of cheap memory. Others highlighted the value in specific niches like bioinformatics and embedded systems where memory is constrained. The discussion also touched on the difficulty of implementing and debugging these structures and the lack of mature libraries in common languages. A compelling comment highlighted the use case of storing large language models efficiently, where succinct data structures can significantly reduce storage requirements and memory access times, potentially enabling new applications on resource-constrained devices. Others noted the theoretical elegance of the approach, even if practical applications remain somewhat niche.
This study experimentally compares bitmap and inverted list compression techniques for accelerating analytical queries on relational databases. Researchers evaluated a range of established and novel compression methods, including Roaring, WAH, Concise, and COMPAX, across diverse datasets and query workloads. The results demonstrate that bitmap compression, specifically Roaring, consistently outperforms inverted lists in terms of query processing time and storage space for most workloads, particularly those with high selectivity or involving multiple attributes. While inverted lists demonstrate some advantages for low-selectivity queries and updates, Roaring bitmaps generally offer a superior balance of performance and efficiency for analytical workloads. The study concludes that careful selection of the compression method based on data characteristics and query patterns is crucial for optimizing analytical query performance.
HN users discussed the trade-offs between bitmap and inverted list compression, focusing on performance in different scenarios. Some highlighted the importance of data characteristics like cardinality and query patterns in determining the optimal choice. Bitmap indexing was noted for its speed with simple queries on high-cardinality attributes but suffers from performance degradation with increasing updates or complex queries. Inverted lists, while generally slower for simple queries, were favored for their efficiency with updates and range queries. Several comments pointed out the paper's age (2017) and questioned the relevance of its findings given advancements in hardware and newer techniques like Roaring bitmaps. There was also discussion of the practical implications for database design and the need for careful benchmarking based on specific use cases.
Iterated Log Coding (ILC) offers a novel approach to data compression by representing integers as a series of logarithmic operations. Instead of traditional methods like Huffman coding or arithmetic coding, ILC leverages the repeated application of the logarithm to achieve potentially superior compression for certain data distributions. It encodes an integer by counting how many times the logarithm base b needs to be applied before the result falls below a threshold. This "iteration count" becomes the core of the compressed representation, supplemented by a fractional value representing the remainder after the final logarithm application. Decoding reverses this process, effectively "exponentiating" the iteration count and incorporating the fractional remainder. While the blog post acknowledges that ILC's practical usefulness requires further investigation, it highlights the theoretical potential and presents a basic implementation in Python.
Hacker News users generally praised the clarity and novelty of the Iterated Log Coding approach. Several commenters appreciated the author's clear explanation of a complex topic and the potential benefits of the technique for compression, especially in specialized domains like bioinformatics. Some discussed its similarities to Huffman coding and Elias gamma coding, suggesting it falls within a family of variable-length codes optimized for certain data distributions. A few pointed out limitations or offered alternative implementations, including using a lookup table for smaller values of 'n' for performance improvements. The practicality of the method for general-purpose compression was questioned, with some suggesting it might be too niche, while others found it theoretically interesting and a valuable addition to existing compression methods.
Lzbench is a compression benchmark focusing on speed, comparing various lossless compression algorithms across different datasets. It prioritizes decompression speed and measures compression ratio, encoding and decoding rates, and RAM usage. The benchmark includes popular algorithms like zstd, lz4, brotli, and deflate, tested on diverse datasets ranging from Silesia Corpus to real-world files like Firefox binaries and game assets. Results are presented interactively, allowing users to filter by algorithm, dataset, and metric, facilitating easy comparison and analysis of compression performance. The project aims to provide a practical, speed-focused overview of how different compression algorithms perform in real-world scenarios.
HN users generally praised the benchmark's visual clarity and ease of use. Several appreciated the inclusion of less common algorithms like Brotli, Lizard, and Zstandard alongside established ones like gzip and LZMA. Some discussed the performance characteristics of different algorithms, noting Zstandard's speed and Brotli's generally good compression. A few users pointed out potential improvements, such as adding more compression levels or providing options to exclude specific algorithms. One commenter wished for pre-compressed benchmark files to reduce load times. The lack of context/meaning for the benchmark data (it uses a "Silesia corpus") was also mentioned.
Bzip3, developed as a modern reimagining of Bzip2, aims to deliver significantly improved compression ratios and speed. It leverages a larger block size, an enhanced Burrows-Wheeler transform, and a more efficient entropy coder based on Asymmetric Numeral Systems (ANS). While maintaining compatibility with the Bzip2 file format for compressed data, Bzip3 boasts compression performance competitive with modern algorithms like zstd and LZMA, coupled with significantly faster decompression than Bzip2. The project's primary goal is to offer a compelling alternative for scenarios requiring robust compression and rapid decompression.
Hacker News users discussed bzip3's performance improvements, particularly its speed increases due to parallelization and its competitive compression ratios compared to bzip2 and other algorithms like zstd and LZMA. Some expressed excitement about its potential and the author's rigorous approach. Several commenters questioned its practical value given the dominance of zstd and the maturity of existing compression tools. Others pointed out that specialized use cases, like embedded systems or situations prioritizing decompression speed, could benefit from bzip3. Some skepticism was voiced about its long-term maintenance given it's a one-person project, alongside curiosity about the new Burrows-Wheeler transform implementation. The use of SIMD and the detailed explanation of design choices in the README were also praised.
This post provides a high-level overview of compression algorithms, categorizing them into lossless and lossy methods. Lossless compression, suitable for text and code, reconstructs the original data perfectly using techniques like Huffman coding and LZ77. Lossy compression, often used for multimedia like images and audio, achieves higher compression ratios by discarding less perceptible data, employing methods such as discrete cosine transform (DCT) and quantization. The post briefly explains the core concepts behind these techniques and illustrates how they reduce data size by exploiting redundancy and irrelevancy. It emphasizes the trade-off between compression ratio and data fidelity, with lossy compression prioritizing smaller file sizes at the expense of some information loss.
Hacker News users discussed various aspects of compression, prompted by a blog post overviewing different algorithms. Several commenters highlighted the importance of understanding data characteristics when choosing a compression method, emphasizing that no single algorithm is universally superior. Some pointed out the trade-offs between compression ratio, speed, and memory usage, with specific examples like LZ77 being fast for decompression but slower for compression. Others discussed more niche compression techniques like ANS and its use in modern codecs, as well as the role of entropy coding. A few users mentioned practical applications and tools, like using zstd for backups and mentioning the utility of brotli. The complexities of lossy compression, particularly for images, were also touched upon.
This blog post presents a different way to derive Shannon entropy, focusing on its property as a unique measure of information content. Instead of starting with desired properties like additivity and then finding a formula that satisfies them, the author begins with a core idea: measuring the average number of binary questions needed to pinpoint a specific outcome from a probability distribution. By formalizing this concept using a binary tree representation of the questioning process and leveraging Kraft's inequality, they demonstrate that -∑pᵢlog₂(pᵢ) emerges naturally as the optimal average question length, thus establishing it as the entropy. This construction emphasizes the intuitive link between entropy and the efficient encoding of information.
Hacker News users discuss the alternative construction of Shannon entropy presented in the linked article. Some express appreciation for the clear explanation and visualizations, finding the geometric approach insightful and offering a fresh perspective on a familiar concept. Others debate the pedagogical value of the approach, questioning whether it truly simplifies understanding for those unfamiliar with entropy, or merely offers a different lens for those already versed in the subject. A few commenters note the connection to cross-entropy and Kullback-Leibler divergence, suggesting the geometric interpretation could be extended to these related concepts. There's also a brief discussion on the practical implications and potential applications of this alternative construction, although no concrete examples are provided. Overall, the comments reflect a mix of appreciation for the novel approach and a pragmatic assessment of its usefulness in teaching and application.
Summary of Comments ( 7 )
https://news.ycombinator.com/item?id=43643441
Hacker News users discussed TVMC's potential applications and limitations. Some highlighted the impressive compression ratios and the potential for wider adoption in areas like game development, VFX, and medical imaging. Others questioned the practicality for real-time applications due to the decompression overhead. Concerns were raised about the project's apparent inactivity and the lack of recent updates, along with the limited file format support. Several commenters expressed interest in GPU decompression and the possibility of integrating TVMC with existing game engines. A key point of discussion revolved around the trade-offs between compression ratio, decompression speed, and visual fidelity.
The Hacker News post titled "TVMC: Time-Varying Mesh Compression" sparked a brief but insightful discussion with a handful of comments focusing on the practical applications and limitations of the presented mesh compression technique.
One commenter highlights the potential of this technology for reducing storage and bandwidth requirements in virtual and augmented reality applications, specifically mentioning the metaverse as a potential beneficiary. They emphasize the importance of efficient mesh compression for creating immersive and interactive experiences in these environments, where detailed 3D models are crucial.
Another comment points out the current limitations of the technology. While acknowledging the potential for various applications, they note that the compression currently works best on meshes with consistent topology over time. This suggests that meshes with significant topological changes, like those seen in simulations with fracturing or merging objects, might not be suitable for this specific compression technique. They also raise the question of whether the demonstrated compression ratios hold true for more complex meshes typically encountered in real-world applications, implicitly suggesting a need for further testing and validation on more diverse datasets.
A third comment focuses on the computational cost associated with the decompression process. While efficient compression is crucial, the commenter rightly points out that if the decompression process is too computationally intensive, it could negate the benefits of reduced storage and bandwidth, especially for real-time applications. They express interest in learning more about the decompression overhead and its impact on performance. This highlights a crucial aspect often overlooked in compression discussions: the trade-off between compression ratio and decompression speed.
Finally, another commenter notes the relevance of this technology to game development, echoing the sentiment about its potential for virtual and augmented reality applications. They also mention the desire for similar compression techniques applicable to skeletal meshes, a common type of mesh used in character animation. This comment reinforces the demand for efficient mesh compression solutions across various domains and highlights the specific needs of different applications, like game development.
In summary, the comments on the Hacker News post demonstrate a general interest in the presented time-varying mesh compression technique, while also acknowledging its limitations and raising important questions regarding its practical applicability, particularly concerning the types of meshes it handles efficiently and the computational cost of decompression.