A Novel High Frequency Encoding Algorithm for Image Compression

In this paper a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the Discrete Cosine Transform (DCT) together with a high frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) Divide the image into blocks and apply DCT to each block; (2) Apply a high frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a Minimized Array; (3) Build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) Apply a delta or differential operator to the list of DC-components; and (5) Apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000

A novel image compression algorithm for high resolution 3D reconstruction

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models