Non-fungible tokens (NFTs) and other Web3 multimedia are typically stored off-chain because image and video assets exceed practical on-chain storage budgets, creating a gap between immutable ownership records and long-term media availability. This paper proposes a collection-level neural compression approach that converts an entire NFT collection into a single compact decoder. The decoder maps a token's integer index directly to its reconstructed image, and is intentionally trained to memorize the collection so that no per-image latent codes are stored. To minimize the decoder's on-chain footprint, we parameterize weights in the frequency domain, progressively prune high-frequency coefficients via zigzag-ordered masking, and apply run-length plus Huffman entropy coding to the resulting sparse parameters. Across three 10,000-image NFT benchmarks, Bored Ape Yacht Club (BAYC), Azuki, and CryptoPunks, the compressed artifacts are reduced to 7.63 MB, 17.09 MB, and 3.33 MB, respectively. These artifacts achieve up to \(177\times\) smaller size than PNG while maintaining high reconstruction quality, measured by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), with PSNR \(\geq\) 33 dB and SSIM \(\geq\) 0.967. We further report Ethereum storage gas estimates showing that collection-level on-chain deployment becomes feasible at \(10^{3}\) – \(10^{4}\) USD under representative fee assumptions, reducing on-chain deployment cost by 58.6%–99.4%. The main contribution is a collection-level compressed decoder that serves as the deployable on-chain storage artifact, removing dependence on external media hosts and avoiding per-image latent storage. These results demonstrate a practical path to self-contained, on-chain availability of large NFT multimedia collections without relying on external storage networks.
Advanced Data Compression Techniques
Advanced Steganography and Watermarking Techniques
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
In this chapter, you will learn how to write, deploy, and interact with smart contracts using Solidity. We will cover fundamental data types, control structures, functions, and contract organization. You will understand the Ethereum Virtual Machine, how contracts send and receive Ether, and how to use events, modifiers, and visibility specifiers. The chapter also introduces reference types like arrays and mappings, common security practices, and techniques for optimizing gas usage.
Bit-decomposition-based zero-knowledge range proofs in the discrete logarithm (DLOG) setting with a transparent setup, e.g., Bulletproof (IEEE S&P 18), Flashproof (ASIACRYPT 22), and SwiftRange (IEEE S&P 24), have garnered widespread popularity across various privacy-enhancing applications. These proofs aim to prove that a committed value falls within the non-negative range [0, 2^N-1] without revealing it, where N represents the bit length of the range. Despite their prevalence, the current implementations still suffer from suboptimal performance. Some exhibit reduced communication costs at the expense of increased computational costs while others experience the opposite. Presently, users are compelled to utilize these proofs in scenarios demanding stringent requirements for both communication and computation efficiency. In this paper, we introduce, FlashSwift, a stronger DLOG-based logarithmic-sized alternative. It stands out for its greater shortness and significantly enhanced computational efficiency compared with the cutting-edge logarithmic-sized ones for the most common ranges where N is no more than 64. It is developed by integrating the techniques from Flashproof and SwiftRange without using a trusted setup. The substantial efficiency gains stem from our dedicated efforts in overcoming the inherent incompatibility barrier between the two techniques. Specifically, when N=64, our proof achieves the same size as Bulletproof and exhibits 1.1 times communication efficiency of SwiftRange. More importantly, compared with the two, it achieves 2.3 times and 1.65 times proving efficiency, and 3.2 times and 1.7 times verification efficiency, respectively. At the time of writing, our proof also creates two new records of the smallest proof sizes, 289 bytes and 417 bytes, for 8-bit and 16-bit ranges among all the bit-decomposition-based ones without requiring trusted setups. Moreover, to the best of our knowledge, it is the first configurable range proof that is adaptable to various scenarios with different specifications, where the configurability allows to trade off communication efficiency for computational efficiency. In addition, we offer a bonus feature: FlashSwift supports the aggregation of multiple single proofs for efficiency improvement. Finally, we provide comprehensive performance benchmarks against the state-of-the-art ones to demonstrate its practicality.
Travis C. Cuvelier, Takashi Tanaka, Robert W. Heath
We propose an adaptive coding approach to achieve linear-quadratic-Gaussian (LQG) control with near-minimum bitrate prefix-free feedback. Our approach combines a recent analysis of a quantizer design for minimum rate LQG control with work on universal lossless source coding for sources on countable alphabets. In the aforementioned quantizer design, it was established that the quantizer outputs are an asymptotically stationary, ergodic process. To enable LQG control with provably near-minimum bitrate, the quantizer outputs must be encoded into binary codewords efficiently. This is possible given knowledge of the probability distributions of the quantizer outputs, or of their limiting distribution. Obtaining such knowledge is challenging; the distributions do not readily admit closed form descriptions. This motivates the application of universal source coding. Our main theoretical contribution in this work is a proof that (after an invertible transformation), the quantizer outputs are random variables that fall within an exponential or power-law envelope class (depending on the plant dimension). Using ideas from universal coding on envelope classes, we develop a practical, zero-delay version of these algorithms that operates with fixed precision arithmetic. We evaluate the performance of this algorithm numerically, and demonstrate competitive results with respect to fundamental tradeoffs between bitrate and LQG control performance.