This Invited Paper presents the 25 years history and the future prospect of JPEG 2000 standards. JPEG 2000 standards (ISO/IEC 15444 series) have been produced by ISO/IEC JTC 1/SC29/WG 1 and common texts are also registered as ITU-T Recommendations (ITU-T T.800 series). JPEG 2000 Part 1 was born in year 2000 and from then around 15 parts have been standardized to compose JPEG 2000 family of standards. Main components of JPEG 2000 are DWT (Discrete Wavelet Transform), EBCOT (Embedded Block Coding with Optimal Truncation) and MQ coder. In JPEG 2000, a lot of functions for image built-up, image exhibition, image processing are integrated in the coding system and various extensions needed for specific applications are provided in the family standards. The coding performance is highly increased from original JPEG standard and JPEG2000 has been adopted in various fields such as Digital Cinema, Document Archives around the world, and PDF specifications. The recent update is the introduction of High-Throughput JPEG 2000 (HTJ2K) and the throughput is improved by an order of magnitude at the expense of slightly reduced coding efficiency. It is expected that the birth of HTJ2K would strongly accelerate the penetration of JPEG 2000 in the near future.

A light field contains a large amount of data because it is represented as a dense set of multi-view images. We propose a method of compressing a light field as a newly emerging representation called a multiplane image (MPI), a graphics-oriented representation composed of a stack of semi-transparent images. Our method was constructed on a deep convolutional neural network (CNN), which was trained to generate an MPI from the given light field. To draw out the potential of this representation, we trained the CNN to be overfitted to the target light field. We also encouraged spatial smoothness to make the MPI easier to compress. Despite being in the early stage of development, our method has already achieved promising rate-distortion performance.

Compressing the sign information of discrete cosine transform (DCT) coefficients is an intractable problem in image coding schemes due to the equiprobable characteristics of the signs. To overcome this difficulty, we propose an efficient compression method for the sign information called “sign retrieval.” This method is inspired by phase retrieval, which is a classical signal restoration problem of finding the phase information of discrete Fourier transform coefficients from their magnitudes. The sign information of all DCT coefficients is excluded from a bitstream at the encoder and is complemented at the decoder through our sign retrieval method. We show through experiments that our method outperforms previous ones in terms of the bit amount for the signs and computation cost. Our method, implemented in Python language, is available from https://github.com/ctsutake/dsr.