Low-complexity multiple description coding of video based on 3D block transforms
EURASIP Journal on Embedded Systems
Warped discrete cosine transform-based low bit-rate block coding using image downsampling
EURASIP Journal on Applied Signal Processing
Down-sampling design in DCT domain with arbitrary ratio for image/video transcoding
IEEE Transactions on Image Processing
Low bit-rate image compression via adaptive down-sampling and constrained least squares upconversion
IEEE Transactions on Image Processing
Perception Based Down Sampling for Low Bit Rate Image Coding
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Adaptive DCT-Domain Down-Sampling and Learning Based Mapping for Low Bit-Rate Image Compression
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A novel low-bit-rate image compression algorithm
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Sparse representation based down-sampling image compression
Journal of Computational and Applied Mathematics
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The most popular lossy image compression method used on the Internet is the JPEG standard. JPEG's good compression performance and low computational and memory complexity make it an attractive method for natural image compression. Nevertheless, as we go to low bit rates that imply lower quality, JPEG introduces disturbing artifacts. It is known that, at low bit rates, a down-sampled image, when JPEG compressed, visually beats the high resolution image compressed via JPEG to be represented by the same number of bits. Motivated by this idea, we show how down-sampling an image to a low resolution, then using JPEG at the lower resolution, and subsequently interpolating the result to the original resolution can improve the overall PSNR performance of the compression process. We give an analytical model and a numerical analysis of the down-sampling, compression and up-sampling process, that makes explicit the possible quality/compression trade-offs. We show that the image auto-correlation can provide a good estimate for establishing the down-sampling factor that achieves optimal performance. Given a specific budget of bits, we determine the down-sampling factor necessary to get the best possible recovered image in terms of PSNR.