Joint thresholding and quantizer selection for transform image coding: entropy-constrained analysis and applications to baseline JPEG

• Published in: IEEE transactions on image processing Vol. 6; no. 2; pp. 285 - 297
• Main Authors: Crouse, M., Ramchandran, K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 1997; Institute of Electrical and Electronics Engineers
• Subjects: Algorithm design and analysis; Applied sciences; Decoding; Encoding; Exact sciences and technology; Huffman coding; Image coding; Image processing; Image quality; Information, signal and communications theory; Pixel; PSNR; Rate-distortion; Signal processing; Telecommunications and information theory; Vector quantization; Performance evaluation; Signal compression; Image processing; Coding; Quantization; Algorithm; Optimization
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.551698

Striving to maximize baseline (Joint Photographers Expert Group-JPEG) image quality without compromising compatibility of current JPEG decoders, we develop an image-adaptive JPEG encoding algorithm that jointly optimizes quantizer selection, coefficient "thresholding", and Huffman coding within a rate-distortion (R-D) framework. Practically speaking, our algorithm unifies two previous approaches to image-adaptive JPEG encoding: R-D optimized quantizer selection and R-D optimal thresholding. Conceptually speaking, our algorithm is a logical consequence of entropy-constrained vector quantization (ECVQ) design principles in the severely constrained instance of JPEG-compatible encoding. We explore both viewpoints: the practical, to concretely derive our algorithm, and the conceptual, to justify the claim that our algorithm approaches the best performance that a JPEG encoder can achieve. This performance includes significant objective peak signal-to-noise ratio (PSNR) improvement over previous work and at high rates gives results comparable to state-of-the-art image coders. For example, coding the Lena image at 1.0 b/pixel, our JPEG encoder achieves a PSNR performance of 39.6 dB that slightly exceeds the quoted PSNR results of Shapiro's wavelet-based zero-tree coder. Using a visually based distortion metric, we can achieve noticeable subjective improvement as well. Furthermore, our algorithm may be applied to other systems that use run-length encoding, including intraframe MPEG and subband or wavelet coding.