With the rapid development of the internet, people's demand for high-quality, high-definition images has significantly increased. As a result, minimizing image size while maintaining quality has become a key industry trend. To meet this need, various image compression formats have emerged, such as WebP and HEIF.
WebP is a modern image format developed by Google that supports both lossy and lossless compression. It uses the VP8 video codec as its core and introduced support for transparency in 2011. Many major websites, including Facebook, now use WebP to deliver faster loading times and better user experiences.
Another notable format is BPG (Better Portable Graphics), created by Fabrice Bellard, a well-known programmer behind projects like FFmpeg and QEMU. BPG uses HEVC (H.265) encoding, offering superior compression efficiency compared to JPEG. In fact, BPG files are typically half the size of equivalent JPEG images. However, due to the high licensing fees associated with HEVC, BPG hasn't gained widespread adoption in the market.
Both WebP and BPG have their strengths and limitations. While they offer good compression, they still rely on traditional codecs. To overcome these challenges and further improve performance, the industry has turned to deep learning techniques for image compression.
Deep learning, particularly through Convolutional Neural Networks (CNNs), has shown great promise in this field. CNNs function as building blocks, consisting of layers such as convolution, pooling, nonlinear activation, and normalization. These networks can extract meaningful features from images, which can then be used for tasks like classification or reconstruction.
In the context of image compression, a typical framework includes modules like the encoder, quantization, inverse quantization, decoder, entropy coding, and rate-distortion optimization. The encoder transforms the image into a compressed feature representation, while the decoder reconstructs the original image from those features. This process allows for efficient compression without significant loss of quality.
To evaluate the effectiveness of an image compression algorithm, three main metrics are commonly used: PSNR (Peak Signal-to-Noise Ratio), BPP (Bits Per Pixel), and MS-SSIM (Multi-Scale Structural Similarity Index). PSNR measures the quality of the reconstructed image, BPP indicates the storage efficiency, and MS-SSIM reflects the perceived visual quality.
For example, when comparing different image formats at the same compression ratio, the Tiny Network Graphics (TNG) format consistently outperforms others in terms of MS-SSIM, indicating better visual quality. Additionally, TNG’s PSNR values often surpass those of commercial formats like WebP and JPEG2000.
When applying deep learning to image compression, the input image is processed through a neural network, producing a compressed feature map. The challenge lies in deciding what type of data to store—floating-point numbers, integers, or binary. Using floating-point numbers results in higher quality but larger file sizes. Therefore, quantization is often applied to convert these values into integers or binary, reducing the bit count per pixel.
Even after quantization, there is still room for improvement. By optimizing the network structure and incorporating techniques like entropy coding, the compression efficiency can be further enhanced. Rate control also plays a vital role in ensuring that the compressed data is distributed efficiently, allowing for even greater reductions in BPP.
In summary, using deep learning for image compression is a promising and rapidly evolving field. It offers the potential for better performance, lower file sizes, and improved visual quality. If you're interested in testing TNG, you can visit the official link (recommended for PC use).
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