JPEG, which was developed 30 years ago, is the most widely used image coding format, especially favored by the resource-deficient devices, due to its simplicity and efficiency. With the evolution of the Internet and the popularity of mobile devices, a huge amount of user-generated JPEG images are uploaded to social media sites like Facebook and Flickr or stored in personal computers or notebooks, which leads to an increase in storage cost. However, the performance of JPEG is far from the-state-of-the art coding methods. Therefore, the lossless recompression of JPEG images is urgent to be studied, which will further reduce the storage cost while maintaining the image fidelity. In this paper, a hybrid coding framework for the lossless recompression of JPEG images (LLJPEG) using transform domain intra prediction is proposed, including block partition and intraprediction, transform and quantization, and entropy coding. Specifically, in LLJPEG, intra prediction is first used to obtain a predicted block. Then the predicted block is transformed by DCT and then quantized to obtain the predicted coefficients. After that, the predicted coefficients are subtracted from the original coefficients to get the DCT coefficient residuals. Finally, the DCT residuals are entropy coded. In LLJPEG, some new coding tools are proposed for intra prediction and the entropy coding is redesigned. The experiments show that LLJPEG can reduce the storage space by 29.43% and 26.40% on the Kodak and DIV2K datasets respectively without any loss for JPEG images, while maintaining low decoding complexity.

High Efficiency Video Coding (HEVC) introduces a number of new coding tools such as 35 intra prediction modes, improved coding unit structure and SAO filter and so on to improve the compression capacity of encoding. Compared with H.264/AVC, the compression performance increased by nearly 50% at the cost of the algorithm complexity increasing significantly. One of the most time-consuming part is the CU partition process which taking over 50% of the encoding time. In this paper, in order to reduce the computational pressure on the encoder side, we design an algorithm based on contour detection and SVM classifier. First, we adopt an improved Canny algorithm which is special designed for CU partition task. Then, based on SVM classifier, we introduce a new CU partition decision strategy. Experimental results show that the proposed algorithm achieves about 50% time saving on average with negligible loss of coding efficiency compared with HEVC reference software HM16.0.