Perceptual Image Codec: What Matters in Practical Learned Image Compression
Summary
PICO (Perceptual Image Codec) is a new learned codec from Apple that is optimized for the human visual system and achieves 2.3–3× bitrate savings over traditional codecs like AV1 and VVC, while running in 230ms encode / 150ms decode on an iPhone 17 Pro Max.
View Cached Full Text
Cached at: 05/24/26, 03:40 PM
Similar Articles
PivCo-Huffman
This paper presents PivCo-Huffman, a new approach to Huffman coding using pivot coding from wavelet trees, enabling high-performance SIMD-friendly encoding and decoding. It consistently outperforms state-of-the-art Huffman codecs and shows how ANS coding can be selectively applied to skewed nodes to approach ANS compression ratios while preserving high decompression speeds.
AdaCodec: A Predictive Visual Code for Video MLLMs
AdaCodec reduces video encoding redundancy in multimodal LLMs by transmitting full visual tokens only when scene prediction fails, otherwise using compact inter-frame change descriptions. It outperforms per-frame RGB baselines at matched token budgets and achieves better or comparable results with significantly fewer tokens, reducing time-to-first-token from 9.26s to 1.62s.
PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
PiD introduces a pixel diffusion decoder that reformulates latent decoding as conditional pixel diffusion, enabling fast and high-quality image synthesis at high resolutions with reduced computational requirements. It decodes latents into 4x or 8x upscaled images in under a second on consumer hardware.
@jiqizhixin: What if your AI could “see” video like a streaming codec—spending tokens only on the most important moments? Introducin…
LLaVA-OneVision-2 introduces codec-stream tokenization for efficient video understanding, significantly outperforming Qwen3-VL-8B on temporal and spatial benchmarks. The model, data, and code are open-sourced.
Data-driven Video Codec with Implicit Neural Representations
This paper presents a video codec that stores video and audio as weights of a sinusoidal representation network, using knowledge distillation and quantization for compression. Experiments show a 2.61x compression ratio compared to the original network, but quality lags behind standard codecs like H.264 and HEVC.