arXiv subjects

Jyrki Alakuijala

Publications and source records attributed to Jyrki Alakuijala.

12 recordsLinked to original sources

arXiv2025

Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric

This paper introduces Zimtohrli, a novel, full-reference audio similarity metric designed for efficient and perceptually accurate quality assessment. In an era dominated by computationally intensive deep learning models and proprietary legacy standards, there is a pressing need for an interpretable, psychoacoustically-grounded metric that balances performance with practicality. Zimtohrli addresses this gap by combining a 128-bin gammatone filterbank front-end, which models the frequency resolution of the cochlea, with a unique non-linear resonator model that mimics the human eardrum's response to acoustic stimuli. Similarity is computed by comparing perceptually-mapped spectrograms using modified Dynamic Time Warping (DTW) and Neurogram Similarity Index Measure (NSIM) algorithms, which incorporate novel non-linearities to better align with human judgment. Zimtohrli achieves superior performance to the baseline open-source ViSQOL metric, and significantly narrows the performance gap with the latest commercial POLQA metric. It offers a compelling balance of perceptual relevance and computational efficiency, positioning it as a strong alternative for modern audio engineering applications, from codec development to the evaluation of generative audio systems.

eess.AS↗

arXiv2025

The JPEG XL Image Coding System: History, Features, Coding Tools, Design Rationale, and Future

JPEG XL is a new image coding system offering state-of-the-art compression performance, lossless JPEG recompression, and advanced features. It aims to replace JPEG, PNG, GIF, and other formats with a single universal codec. This article provides an overview of JPEG XL, including its history, design rationale, coding tools, and future potential. It can be used as a companion document to the standard (ISO/IEC 18181), or as a standalone article to better understand JPEG XL, either at a high level or in considerable technical detail.

cs.MM↗

arXiv2024

Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction

The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates where life emerges is a marked shift in dynamics when self-replication appears. While there are some hypotheses regarding how self-replicators arose in nature, we know very little about the general dynamics, computational principles, and necessary conditions for self-replicators to emerge. This is especially true on "computational substrates" where interactions involve logical, mathematical, or programming rules. In this paper we take a step towards understanding how self-replicators arise by studying several computational substrates based on various simple programming languages and machine instruction sets. We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise. We demonstrate how this occurs due to random interactions and self-modification, and can happen with and without background random mutations. We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators. Finally, we show a counterexample of a minimalistic programming language where self-replicators are possible, but so far have not been observed to arise.

cs.NE↗

arXiv2024

Users prefer Jpegli over same-sized libjpeg-turbo or MozJPEG

We performed pairwise comparisons by human raters of JPEG images from MozJPEG, libjpeg-turbo and our new Jpegli encoder. When compressing images at a quality similar to libjpeg-turbo quality 95, the Jpegli images were 54% likely to be preferred over both libjpeg-turbo and MozJPEG images, but used only 2.8 bits per pixel compared to libjpeg-turbo and MozJPEG that used 3.8 and 3.5 bits per pixel respectively. The raw ratings and source images are publicly available for further analysis and study.

eess.IV↗

arXiv2020

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation

The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically-plausible alpha synaptic transfer function. Additionally, we use trainable synchronisation pulses that provide bias, add flexibility during training and exploit the decay part of the alpha function. We show that such networks can be trained successfully on noisy Boolean logic tasks and on the MNIST dataset encoded in time. The results show that the spiking neural network outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. We also find that the spiking network spontaneously discovers two operating regimes, mirroring the accuracy-speed trade-off observed in human decision-making: a slow regime, where a decision is taken after all hidden neurons have spiked and the accuracy is very high, and a fast regime, where a decision is taken very fast but the accuracy is lower. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks towards energy-efficient and more complex biologically-inspired neural architectures.

cs.NE↗

arXiv2020

Intelligent Matrix Exponentiation

We present a novel machine learning architecture that uses the exponential of a single input-dependent matrix as its only nonlinearity. The mathematical simplicity of this architecture allows a detailed analysis of its behaviour, providing robustness guarantees via Lipschitz bounds. Despite its simplicity, a single matrix exponential layer already provides universal approximation properties and can learn fundamental functions of the input, such as periodic functions or multivariate polynomials. This architecture outperforms other general-purpose architectures on benchmark problems, including CIFAR-10, using substantially fewer parameters.

cs.LG↗

arXiv2019

Committee Draft of JPEG XL Image Coding System

JPEG XL is a practical approach focused on scalable web distribution and efficient compression of high-quality images. It provides various benefits compared to existing image formats: 60% size reduction at equivalent subjective quality; fast, parallelizable decoding and encoding configurations; features such as progressive, lossless, animation, and reversible transcoding of existing JPEG with 22% size reduction; support for high-quality applications including wide gamut, higher resolution/bit depth/dynamic range, and visually lossless coding. The JPEG XL architecture is traditional block-transform coding with upgrades to each component.

eess.IV↗

arXiv2018

Randen - fast backtracking-resistant random generator with AES+Feistel+Reverie

Algorithms that rely on a pseudorandom number generator often lose their performance guarantees when adversaries can predict the behavior of the generator. To protect non-cryptographic applications against such attacks, we propose 'strong' pseudorandom generators characterized by two properties: computationally indistinguishable from random and backtracking-resistant. Some existing cryptographically secure generators also meet these criteria, but they are too slow to be accepted for general-purpose use. We introduce a new open-sourced generator called 'Randen' and show that it is 'strong' in addition to outperforming Mersenne Twister, PCG, ChaCha8, ISAAC and Philox in real-world benchmarks. This is made possible by hardware acceleration. Randen is an instantiation of Reverie, a recently published robust sponge-like random generator, with a new permutation built from an improved generalized Feistel structure with 16 branches. We provide new bounds on active s-boxes for up to 24 rounds of this construction, made possible by a memory-efficient search algorithm. Replacing existing generators with Randen can protect randomized algorithms such as reservoir sampling from attack. The permutation may also be useful for wide-block ciphers and hashing functions.

cs.CR↗

arXiv2018

Noise generation for compression algorithms

In various Computer Vision and Signal Processing applications, noise is typically perceived as a drawback of the image capturing system that ought to be removed. We, on the other hand, claim that image noise, just as texture, is important for visual perception and, therefore, critical for lossy compression algorithms that tend to make decompressed images look less realistic by removing small image details. In this paper we propose a physically and biologically inspired technique that learns a noise model at the encoding step of the compression algorithm and then generates the appropriate amount of additive noise at the decoding step. Our method can significantly increase the realism of the decompressed image at the cost of few bytes of additional memory space regardless of the original image size. The implementation of our method is open-sourced and available at https://github.com/google/pik.

cs.CV↗

arXiv2017

Users prefer Guetzli JPEG over same-sized libjpeg

We report on pairwise comparisons by human raters of JPEG images from libjpeg and our new Guetzli encoder. Although both files are size-matched, 75% of ratings are in favor of Guetzli. This implies the Butteraugli psychovisual image similarity metric which guides Guetzli is reasonably close to human perception at high quality levels. We provide access to the raw ratings and source images for further analysis and study.

cs.CV↗

arXiv2017

Guetzli: Perceptually Guided JPEG Encoder

Guetzli is a new JPEG encoder that aims to produce visually indistinguishable images at a lower bit-rate than other common JPEG encoders. It optimizes both the JPEG global quantization tables and the DCT coefficient values in each JPEG block using a closed-loop optimizer. Guetzli uses Butteraugli, our perceptual distance metric, as the source of feedback in its optimization process. We reach a 29-45% reduction in data size for a given perceptual distance, according to Butteraugli, in comparison to other compressors we tried. Guetzli's computation is currently extremely slow, which limits its applicability to compressing static content and serving as a proof- of-concept that we can achieve significant reductions in size by combining advanced psychovisual models with lossy compression techniques.

cs.CV↗

arXiv2017

Fast keyed hash/pseudo-random function using SIMD multiply and permute

HighwayHash is a new pseudo-random function based on SIMD multiply and permute instructions for thorough and fast hashing. It is 5.2 times as fast as SipHash for 1 KiB inputs. An open-source implementation is available under a permissive license. We discuss design choices and provide statistical analysis, speed measurements and preliminary cryptanalysis. Assuming it withstands further analysis, strengthened variants may also substantially accelerate file checksums and stream ciphers.

cs.CR↗